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*.pdf
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*/raw/*
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raw
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*.zip
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dataset_OG/
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# v large files in the dataset
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dataset_IV/Blattman2014/data/apsr_Blattman_etal_2014.csv
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dataset_IV/Coppock2016/data/ajps_Coppock_etal_2016.csv
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dataset_IV/Lelkes2017/data/ajps_Lelkes_2017.csv
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| 13 |
+
dataset_IV/Ziaja2020/data/jop_Ziaja_2020.csv
|
| 14 |
+
|
repo-type=dataset/README.md
ADDED
|
@@ -0,0 +1,55 @@
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
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|
|
|
|
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|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
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|
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|
|
|
| 1 |
+
# Causal-Benchmark-Dataset
|
| 2 |
+
|
| 3 |
+
A curated dataset for evaluating **automated causal reasoning** systems end-to-end—from identification to estimation.
|
| 4 |
+
|
| 5 |
+
## Goal
|
| 6 |
+
|
| 7 |
+
Given the inherently subjective nature of causal estimation, we evaluate systems in two stages:
|
| 8 |
+
|
| 9 |
+
1. **Identification:** Specify the identification assumptions that justify the causal estimand.
|
| 10 |
+
2. **Estimation:** Given a valid identification strategy, implement and compare estimation procedures. While many papers rely on classical methods (e.g., linear regression, TWFE), we also evaluate modern approaches (e.g., DML-style estimators).
|
| 11 |
+
|
| 12 |
+
## Schemas
|
| 13 |
+
|
| 14 |
+
- **Identification Schema:** Defines the required and optional fields for an identification strategy. This is the **minimum information** needed to answer a causal query.
|
| 15 |
+
- **Causal Finding Schema:** Supports dataset construction and rapid validation of generated questions and answers.
|
| 16 |
+
|
| 17 |
+
## Inputs to the Causal Reasoner
|
| 18 |
+
|
| 19 |
+
For each task, the reasoner receives:
|
| 20 |
+
|
| 21 |
+
1. `question.txt` — a brief (1–2 lines) causal query.
|
| 22 |
+
2. **Dataset** — a CSV file.
|
| 23 |
+
3. `metadata.txt` — documentation describing:
|
| 24 |
+
- How the data were collected and the study context,
|
| 25 |
+
- Explanations for all relevant columns (and, where possible, other columns as well).
|
| 26 |
+
|
| 27 |
+
## Expected Outputs
|
| 28 |
+
|
| 29 |
+
Each query currently produces two artifacts:
|
| 30 |
+
|
| 31 |
+
1. **`identification_strategy.json`** — a JSON file conforming strictly to the Identification Schema.
|
| 32 |
+
2. **Estimation code (Python)** — typically **two** implementations per query:
|
| 33 |
+
- A replication of the method used in the original paper,
|
| 34 |
+
- A modern DML-based approach.
|
| 35 |
+
|
| 36 |
+
> **Note:** Both estimation implementations must correspond to the **same** identification strategy.
|
| 37 |
+
|
| 38 |
+
## Dataset Status (WIP)
|
| 39 |
+
|
| 40 |
+
- **IV:** ~26 papers
|
| 41 |
+
- **DiD:** ~40 papers
|
| 42 |
+
- **RDD:** ~50 papers
|
| 43 |
+
|
| 44 |
+
## Roadmap / To-Do
|
| 45 |
+
|
| 46 |
+
- **Add textbook datasets**, e.g.:
|
| 47 |
+
- *Causal ML* book datasets
|
| 48 |
+
- `R` datasets - https://cran.r-project.org/web/packages/causaldata/causaldata.pdf
|
| 49 |
+
- **QR** dataset collection (OpenIntro Statistics, *Quantitative Social Science*, *Causal Inference for the Brave and True*, etc.) - https://github.com/xxxiaol/QRData
|
| 50 |
+
- **Ingest additional papers/benchmarks** (relatively straightforward):
|
| 51 |
+
- REPRO-Bench (UIUC Kang Lab): <https://github.com/uiuc-kang-lab/REPRO-Bench>
|
| 52 |
+
- Hugging Face mirror: <https://huggingface.co/datasets/chuxuan/REPRO-Bench/tree/main/4>
|
| 53 |
+
- Large-scale study they target: *Mass Reproducibility and Replicability: A New Hope* (IZA DP 16912): <https://www.iza.org/publications/dp/16912/mass-reproducibility-and-replicability-a-new-hope>
|
| 54 |
+
|
| 55 |
+
|
repo-type=dataset/causal_finding_schema.py
ADDED
|
@@ -0,0 +1,70 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from identification_schema_new import *
|
| 2 |
+
from identification_schema_new import Identification
|
| 3 |
+
from typing import List, Optional, Union, Dict
|
| 4 |
+
from pydantic import BaseModel, Field
|
| 5 |
+
from typing import List, Optional, Union, Literal, Dict, Any
|
| 6 |
+
from pydantic import BaseModel, Field, root_validator
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
# ---------- results payload ----------
|
| 13 |
+
class ConfidenceInterval(BaseModel):
|
| 14 |
+
lower: float = Field(..., description="Lower bound of the confidence interval.")
|
| 15 |
+
upper: float = Field(..., description="Upper bound of the confidence interval.")
|
| 16 |
+
level: float = Field(0.95, ge=0, le=1, description="Nominal level (e.g., 0.95 for a 95% CI).")
|
| 17 |
+
|
| 18 |
+
class CausalFinding(BaseModel):
|
| 19 |
+
# Identification (replaces IdentificationMethod literal AND removes duplicate variable fields)
|
| 20 |
+
identification_strategy: Identification = Field(..., description="Fully specified identification design (one of the classes above). THE COLUMN NAMES SHOULD BE IN THE DATASET CSV!")
|
| 21 |
+
estimation_strategy_description: Optional[str] = Field( None, description="Description of the estimation strategy used to compute the reported number.")
|
| 22 |
+
|
| 23 |
+
# Reported results
|
| 24 |
+
quantity_value: Optional[float] = Field(None, description="Point estimate as reported; no unit transforms.")
|
| 25 |
+
quantity_ci: Optional[ConfidenceInterval] = Field(None, description="Reported CI, if available. Report only if upper, lower and level is known.")
|
| 26 |
+
standard_error: Optional[float] = Field(None, description="Reported SE, if available.")
|
| 27 |
+
p_value: Optional[float] = Field(None, description="Reported p-value, if available.")
|
| 28 |
+
effect_units: Optional[str] = Field(None, description="Units/scale (pp, log points, dollars, SDs).")
|
| 29 |
+
|
| 30 |
+
evidence_value_quote: Optional[str] = Field(None, description="≤30-word quote in the paper with numerical result.")
|
| 31 |
+
evidence_quote: Optional[str] = Field(None, description="≤30-word quote from the paper supporting the identification strategy.")
|
| 32 |
+
evidence_source: Optional[str] = Field(None, description="Where found (e.g., 'Table 2, p.14') in the paper.")
|
| 33 |
+
|
| 34 |
+
# population: Optional[str] = Field(
|
| 35 |
+
# None, description="Human-readable description of the study population (subset of the dataset) or any row filter logic used to select the population."
|
| 36 |
+
# )
|
| 37 |
+
|
| 38 |
+
subgroup: Optional[str] = Field(None, description="Heterogeneity slice (e.g., females, low-income), if any.")
|
| 39 |
+
|
| 40 |
+
exact_causal_question: Optional[str] = Field(None, description="Precise causal question explaining the correct quantitily (average tratement effect on trated/ conditional treatement .... ). Note that the question should NOT reveal the identification strategy. You need not use the same colum")
|
| 41 |
+
# dataset_context: Optional[str] = Field(None, description="1–2 sentences on where this question was provided in the paper.")
|
| 42 |
+
layman_query: Optional[str] = Field(
|
| 43 |
+
None,
|
| 44 |
+
description=("≤2 sentences in simple language matching the estimand; "
|
| 45 |
+
"avoid method names and CSV column names.")
|
| 46 |
+
)
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
class CausalFindingGenerated(BaseModel):
|
| 50 |
+
# Identification (replaces IdentificationMethod literal AND removes duplicate variable fields)
|
| 51 |
+
identification_strategy: Identification = Field(..., description="Fully specified identification design for DiD. THE COLUMN NAMES SHOULD BE IN THE DATASET!")
|
| 52 |
+
estimand_population: Optional[str] = Field(
|
| 53 |
+
None, description="Population/subgroup the estimand refers to (e.g., 'compliers', 'treated')."
|
| 54 |
+
)
|
| 55 |
+
|
| 56 |
+
# Reported results
|
| 57 |
+
quantity_value: Optional[float] = Field(None, description="Point estimate ; no unit transforms.")
|
| 58 |
+
quantity_ci: Optional[ConfidenceInterval] = Field(None, description="Reported CI, if available. Report only if upper, lower and level is known.")
|
| 59 |
+
standard_error: Optional[float] = Field(None, description="Reported SE, if available.")
|
| 60 |
+
p_value: Optional[float] = Field(None, description="Reported p-value, if available.")
|
| 61 |
+
effect_units: Optional[str] = Field(None, description="Units/scale (pp, log points, dollars, SDs).")
|
| 62 |
+
|
| 63 |
+
subgroup: Optional[str] = Field(None, description="Heterogeneity slice (e.g., females, low-income), if any.")
|
| 64 |
+
|
| 65 |
+
exact_causal_question: Optional[str] = Field(None, description="Precise causal question in the paper. This should the an exact question defined technially and non ambiguous. Mention the sepcific treatment/periods/groups if there are multiple. ")
|
| 66 |
+
layman_query: Optional[str] = Field(
|
| 67 |
+
None,
|
| 68 |
+
description=("≤2 sentences in simple language matching the estimand; "
|
| 69 |
+
"avoid method names and CSV column names.")
|
| 70 |
+
)
|
repo-type=dataset/identification_schema.py
ADDED
|
@@ -0,0 +1,73 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from typing import List, Optional, Union, Dict
|
| 2 |
+
from pydantic import BaseModel, Field
|
| 3 |
+
from typing import Literal
|
| 4 |
+
# ---------- Common primitives ----------
|
| 5 |
+
|
| 6 |
+
CausalQuantity = Literal["ATE","ATT","ATC","LATE","CATE","CATT","CLATE","Other/Unclear"]
|
| 7 |
+
|
| 8 |
+
class IdentificationBase(BaseModel):
|
| 9 |
+
strategy: str = Field(..., description="Name of the identification strategy.")
|
| 10 |
+
variant: Optional[str] = Field(None, description="Variant/subtype of the strategy (e.g., 'sharp', 'fuzzy', 'staggered', 'encouragement design' etc depending on the strategy).")
|
| 11 |
+
treatments: List[str] = Field(..., description="Treatment column name(s).")
|
| 12 |
+
outcomes: List[str] = Field(..., description="Outcome column name(s).")
|
| 13 |
+
controls: Optional[List[str]] = Field(
|
| 14 |
+
None, description="Column names of covariates controls used in analysis."
|
| 15 |
+
)
|
| 16 |
+
post_treatment_variables: Optional[List[str]] = Field(
|
| 17 |
+
None, description="Column names of post-treatment variables (e.g., mediators, colliders). Must provide if there are in the dataset."
|
| 18 |
+
)
|
| 19 |
+
minimal_controlling_set: Optional[List[str]] = Field(
|
| 20 |
+
None, description="Column names of a minimal sufficient adjustment set (if applicable). For example to get conditional exogeneity, or to satisfy conditional parallel trends assumption."
|
| 21 |
+
)
|
| 22 |
+
reason_for_minimal_controlling_set: Optional[str] = Field(
|
| 23 |
+
None, description="If applicable (minimal_controlling_set is not null) provide quotable text from the paper reasoning for the minimal controlling set.")
|
| 24 |
+
causal_quantity: CausalQuantity = Field(..., description="Causal estimand (ATE/ATT/LATE/etc.).")
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
class RCT(IdentificationBase):
|
| 28 |
+
strategy: Literal["RCT"] = Field(
|
| 29 |
+
"RCT", description="Randomized controlled trial."
|
| 30 |
+
)
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
class ConditionalExogeneity(IdentificationBase):
|
| 34 |
+
strategy: Literal["Conditional Exogeneity"] = Field(
|
| 35 |
+
"Conditional Exogeneity", description="Selection on observables."
|
| 36 |
+
)
|
| 37 |
+
minimal_controlling_set: List[str] = Field(
|
| 38 |
+
..., description="Column names of covariates assumed to render treatment independent of potential outcomes."
|
| 39 |
+
)
|
| 40 |
+
|
| 41 |
+
class InstrumentalVariable(IdentificationBase):
|
| 42 |
+
strategy: Literal["Instrumental Variable"] = Field(
|
| 43 |
+
"Instrumental Variable", description="Instrumental-variables design."
|
| 44 |
+
)
|
| 45 |
+
is_encouragement_design: bool = Field(..., description="Whether the IV design is an encouragement design.")
|
| 46 |
+
instrument: List[str] = Field(..., description="Instrument column name(s).")
|
| 47 |
+
|
| 48 |
+
class RegressionDiscontinuity(IdentificationBase):
|
| 49 |
+
strategy: Literal["Regression Discontinuity"] = Field(
|
| 50 |
+
"Sharp Regression Discontinuity", description="Regression discontinuity design. Sharp RD."
|
| 51 |
+
)
|
| 52 |
+
variant: Literal["sharp", "fuzzy"] = Field(..., description="Variant of the regression discontinuity design (sharp or fuzzy).")
|
| 53 |
+
running_variable: str = Field(..., description="Running/score variable.")
|
| 54 |
+
cutoff: float = Field(..., description="Threshold value on the running variable.")
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
class DifferenceInDifferences(IdentificationBase):
|
| 58 |
+
strategy: Literal["Difference-in-Differences"] = Field(
|
| 59 |
+
"Difference-in-Differences", description="Difference-in-Differences design."
|
| 60 |
+
)
|
| 61 |
+
time_variable: str = Field(..., description="Column names of the time index variable.")
|
| 62 |
+
group_variable: str = Field(..., description="Column names of the unit/group identifier.")
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
Identification = Union[
|
| 66 |
+
RCT,
|
| 67 |
+
ConditionalExogeneity,
|
| 68 |
+
InstrumentalVariable,
|
| 69 |
+
RegressionDiscontinuity,
|
| 70 |
+
DifferenceInDifferences,
|
| 71 |
+
]
|
| 72 |
+
|
| 73 |
+
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/data/Bischof_Wagner_2019_AJPS.csv
ADDED
|
@@ -0,0 +1,553 @@
|
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|
| 1 |
+
country,year,decade,cltreat,polarization,rtreatment,gdpgrowth,lunemployment,lparpol,lenp,threshold
|
| 2 |
+
Belgium,1973.0,0.0,1.0,2.472161293029785,0.0,6.047488793172985,,12.822805404663086,5.599999904632568,0.0
|
| 3 |
+
Belgium,1974.0,0.0,2.0,,0.0,6.047488793172985,2.200000047683716,12.822805404663086,5.599999904632568,0.0
|
| 4 |
+
Belgium,1975.0,0.0,2.0,,0.0,6.047488793172985,2.299999952316284,16.763500213623047,5.599999904632568,0.0
|
| 5 |
+
Belgium,1976.0,0.0,2.0,2.3078856468200684,0.0,5.464140013857102,4.199999809265137,16.763500213623047,5.599999904632568,0.0
|
| 6 |
+
Belgium,1977.0,0.0,3.0,2.3042147159576416,0.0,0.501978630361009,5.5,16.763500213623047,5.599999904632568,0.0
|
| 7 |
+
Belgium,1978.0,0.0,4.0,2.2457361221313477,1.0,2.745989881186004,6.300000190734863,12.078908920288086,5.099999904632568,0.0
|
| 8 |
+
Belgium,1979.0,0.0,4.0,2.1895828247070312,1.0,2.249127602851671,6.800000190734863,13.722413063049316,6.5,0.0
|
| 9 |
+
Belgium,1980.0,1.0,4.0,2.402082920074463,1.0,4.3290084470681895,7.0,13.722413063049316,6.5,0.0
|
| 10 |
+
Belgium,1981.0,1.0,5.0,2.2335174083709717,1.0,-0.2766538111496857,7.400000095367432,13.722413063049316,6.5,0.0
|
| 11 |
+
Belgium,1982.0,1.0,5.0,2.1849758625030518,1.0,0.622329238332691,9.399999618530273,17.23249053955078,7.300000190734863,0.0
|
| 12 |
+
Belgium,1983.0,1.0,5.0,1.98067045211792,1.0,0.3198123133602614,11.0,17.23249053955078,7.300000190734863,0.0
|
| 13 |
+
Belgium,1984.0,1.0,5.0,2.0490477085113525,1.0,2.467921390155771,10.699999809265137,17.23249053955078,7.300000190734863,0.0
|
| 14 |
+
Belgium,1985.0,1.0,6.0,2.22017765045166,1.0,1.6215189393805063,10.800000190734863,17.23249053955078,7.300000190734863,0.0
|
| 15 |
+
Belgium,1986.0,1.0,6.0,2.093250036239624,1.0,1.7864699489792006,10.100000381469727,14.10129451751709,6.900000095367432,0.0
|
| 16 |
+
Belgium,1987.0,1.0,7.0,2.027662992477417,1.0,2.2194779692617677,10.0,14.10129451751709,6.900000095367432,0.0
|
| 17 |
+
Belgium,1988.0,1.0,7.0,2.1451563835144043,1.0,4.390795049076455,9.800000190734863,9.968611717224121,7.099999904632568,0.0
|
| 18 |
+
Belgium,1989.0,1.0,7.0,2.0423662662506104,1.0,3.093998963904559,8.800000190734863,9.968611717224121,7.099999904632568,0.0
|
| 19 |
+
Belgium,1990.0,2.0,7.0,1.798876166343689,1.0,2.830268093963073,7.400000095367432,9.968611717224121,7.099999904632568,0.0
|
| 20 |
+
Belgium,1991.0,2.0,8.0,1.932600498199463,1.0,1.4553717402542197,6.599999904632568,9.968611717224121,7.099999904632568,0.0
|
| 21 |
+
Belgium,1992.0,2.0,8.0,2.0913937091827393,1.0,1.1195657272665898,6.400000095367432,5.39808464050293,8.100000381469727,0.0
|
| 22 |
+
Belgium,1993.0,2.0,8.0,2.110870122909546,1.0,-1.3479994821868688,7.099999904632568,5.39808464050293,8.100000381469727,0.0
|
| 23 |
+
Belgium,1994.0,2.0,8.0,2.2272071838378906,1.0,2.9093187041587294,8.600000381469727,5.39808464050293,8.100000381469727,0.0
|
| 24 |
+
Belgium,1995.0,2.0,9.0,1.9461640119552612,1.0,2.1705502147694307,9.800000190734863,5.39808464050293,8.100000381469727,0.0
|
| 25 |
+
Belgium,1996.0,2.0,9.0,1.9998753070831299,1.0,1.3948447683273575,9.699999809265137,8.24034595489502,7.800000190734863,0.0
|
| 26 |
+
Belgium,1997.0,2.0,9.0,2.0763728618621826,1.0,3.459881781522691,9.5,8.24034595489502,7.800000190734863,0.0
|
| 27 |
+
Belgium,1998.0,2.0,9.0,2.128234386444092,1.0,1.7579350087414178,9.199999809265137,8.24034595489502,7.800000190734863,0.0
|
| 28 |
+
Belgium,1999.0,2.0,10.0,2.044025182723999,1.0,3.3259566217057106,9.300000190734863,8.24034595489502,7.800000190734863,0.0
|
| 29 |
+
Belgium,2000.0,3.0,10.0,1.9067168235778809,1.0,3.382791107391378,8.5,7.7800612449646,8.899999618530273,0.0
|
| 30 |
+
Belgium,2001.0,3.0,10.0,1.8612130880355835,1.0,0.46531742875591225,6.900000095367432,7.7800612449646,8.899999618530273,0.0
|
| 31 |
+
Belgium,2002.0,3.0,10.0,1.9248919486999512,1.0,1.3252231283896323,6.599999904632568,7.7800612449646,8.899999618530273,0.0
|
| 32 |
+
Belgium,2003.0,3.0,11.0,1.9264068603515625,1.0,0.3535339590101152,7.5,7.7800612449646,8.899999618530273,0.0
|
| 33 |
+
Belgium,2004.0,3.0,11.0,2.0327134132385254,1.0,3.18725075330989,8.199999809265137,10.805374145507812,6.900000095367432,0.0
|
| 34 |
+
Belgium,2005.0,3.0,11.0,1.970250129699707,1.0,1.5341880847874378,8.399999618530273,10.805374145507812,6.900000095367432,0.0
|
| 35 |
+
Belgium,2006.0,3.0,11.0,1.8972090482711792,1.0,1.8256453656872023,8.5,10.805374145507812,6.900000095367432,0.0
|
| 36 |
+
Belgium,2007.0,3.0,12.0,1.932173728942871,1.0,2.6409733996210556,8.300000190734863,10.805374145507812,6.900000095367432,0.0
|
| 37 |
+
Belgium,2008.0,3.0,12.0,1.968093752861023,1.0,-0.04564021654068894,7.5,23.005712509155273,8.699999809265137,0.0
|
| 38 |
+
Belgium,2009.0,3.0,12.0,2.081117630004883,1.0,-3.067939972481895,7.0,23.005712509155273,8.699999809265137,0.0
|
| 39 |
+
Belgium,2010.0,4.0,13.0,1.9672813415527344,1.0,1.7608382930536943,7.900000095367432,23.005712509155273,8.300000190734863,0.0
|
| 40 |
+
Belgium,2011.0,4.0,13.0,1.965515375137329,1.0,0.3948137958593098,8.300000190734863,24.603906631469727,8.300000190734863,0.0
|
| 41 |
+
Belgium,2012.0,4.0,13.0,1.8836809396743774,1.0,-0.5658932812887087,7.199999809265137,24.603906631469727,8.300000190734863,0.0
|
| 42 |
+
Belgium,2013.0,4.0,13.0,1.8112293481826782,1.0,-0.4849486000316041,7.599999904632568,24.603906631469727,8.300000190734863,0.0
|
| 43 |
+
Belgium,2014.0,4.0,14.0,1.844891905784607,1.0,0.8589848164374416,8.399999618530273,,,0.0
|
| 44 |
+
Belgium,2015.0,4.0,14.0,1.9357095956802368,1.0,0.884319322112202,8.5,,,0.0
|
| 45 |
+
Belgium,2016.0,4.0,14.0,1.8901410102844238,1.0,0.884319322112202,7.800000190734863,,,0.0
|
| 46 |
+
Bulgaria,2004.0,3.0,15.0,2.350879430770874,0.0,7.136139297324925,,10.404668807983398,2.9000000953674316,1.0
|
| 47 |
+
Bulgaria,2005.0,3.0,16.0,2.440648078918457,1.0,7.807863503811674,12.100000381469727,10.404668807983398,2.9000000953674316,1.0
|
| 48 |
+
Bulgaria,2006.0,3.0,16.0,2.4209320545196533,1.0,7.319968099310807,10.100000381469727,14.525321006774902,4.800000190734863,1.0
|
| 49 |
+
Bulgaria,2007.0,3.0,16.0,2.3881595134735107,1.0,9.868461615260571,9.0,14.525321006774902,4.800000190734863,1.0
|
| 50 |
+
Bulgaria,2008.0,3.0,16.0,2.47114634513855,1.0,6.3911744793259295,6.900000095367432,14.525321006774902,4.800000190734863,1.0
|
| 51 |
+
Bulgaria,2009.0,3.0,17.0,2.4457812309265137,1.0,-3.600640426278945,5.599999904632568,14.525321006774902,4.800000190734863,1.0
|
| 52 |
+
Bulgaria,2010.0,4.0,17.0,2.2670915126800537,1.0,0.7153651510048873,6.800000190734863,19.3847713470459,3.299999952316284,1.0
|
| 53 |
+
Bulgaria,2011.0,4.0,17.0,2.249687671661377,1.0,2.237452688729836,10.300000190734863,19.3847713470459,3.299999952316284,1.0
|
| 54 |
+
Bulgaria,2012.0,4.0,17.0,2.3271234035491943,1.0,0.8193523775486973,11.300000190734863,19.3847713470459,3.299999952316284,1.0
|
| 55 |
+
Bulgaria,2013.0,4.0,18.0,2.337162494659424,1.0,1.8502953979632368,12.300000190734863,19.3847713470459,3.299999952316284,1.0
|
| 56 |
+
Bulgaria,2014.0,4.0,19.0,2.5315074920654297,1.0,2.1288427793818983,13.0,16.70408058166504,3.0999999046325684,1.0
|
| 57 |
+
Bulgaria,2015.0,4.0,19.0,2.6530961990356445,1.0,3.626237861630199,11.399999618530273,13.74667739868164,5.099999904632568,1.0
|
| 58 |
+
Bulgaria,2016.0,4.0,19.0,2.5997369289398193,1.0,3.626237861630199,9.199999809265137,13.74667739868164,5.099999904632568,1.0
|
| 59 |
+
Denmark,1973.0,0.0,20.0,1.9336578845977783,0.0,3.1339190585172516,,27.33475112915039,3.9000000953674316,1.0
|
| 60 |
+
Denmark,1974.0,0.0,20.0,,0.0,3.1339190585172516,0.699999988079071,23.803625106811523,6.800000190734863,1.0
|
| 61 |
+
Denmark,1975.0,0.0,21.0,,0.0,3.1339190585172516,2.799999952316284,23.803625106811523,6.800000190734863,1.0
|
| 62 |
+
Denmark,1976.0,0.0,21.0,1.89829421043396,0.0,5.827168399205965,3.9000000953674316,19.476245880126953,5.400000095367432,1.0
|
| 63 |
+
Denmark,1977.0,0.0,22.0,1.8372530937194824,0.0,1.6617394443624456,5.099999904632568,19.476245880126953,5.400000095367432,1.0
|
| 64 |
+
Denmark,1978.0,0.0,22.0,1.8887524604797363,0.0,1.9573787044925213,5.900000095367432,22.462299346923828,5.099999904632568,1.0
|
| 65 |
+
Denmark,1979.0,0.0,23.0,1.8282994031906128,0.0,3.6951093982642735,6.599999904632568,22.462299346923828,5.099999904632568,1.0
|
| 66 |
+
Denmark,1980.0,1.0,23.0,1.751944661140442,0.0,-0.6084795541796443,4.599999904632568,38.41429138183594,4.800000190734863,1.0
|
| 67 |
+
Denmark,1981.0,1.0,24.0,1.9252485036849976,0.0,-0.8587465402306249,4.900000095367432,38.41429138183594,4.800000190734863,1.0
|
| 68 |
+
Denmark,1982.0,1.0,24.0,1.8455822467803955,0.0,3.790220820942569,7.900000095367432,31.110136032104492,5.5,1.0
|
| 69 |
+
Denmark,1983.0,1.0,24.0,1.9543052911758423,0.0,2.722368593234091,8.399999618530273,31.110136032104492,5.5,1.0
|
| 70 |
+
Denmark,1984.0,1.0,25.0,1.9038054943084717,0.0,4.220149912272302,8.399999618530273,31.110136032104492,5.5,1.0
|
| 71 |
+
Denmark,1985.0,1.0,25.0,1.9424455165863037,0.0,3.9822851384943094,7.900000095367432,30.530744552612305,5.0,1.0
|
| 72 |
+
Denmark,1986.0,1.0,25.0,1.9442025423049927,0.0,4.8089883972314444,6.699999809265137,30.530744552612305,5.0,1.0
|
| 73 |
+
Denmark,1987.0,1.0,26.0,2.018145799636841,0.0,0.1629856612940956,5.0,30.530744552612305,5.0,1.0
|
| 74 |
+
Denmark,1988.0,1.0,27.0,2.2277047634124756,0.0,-0.19117637567232282,5.0,25.490615844726562,5.300000190734863,1.0
|
| 75 |
+
Denmark,1989.0,1.0,27.0,2.1200990676879883,0.0,0.512587155929906,5.699999809265137,27.542861938476562,5.300000190734863,1.0
|
| 76 |
+
Denmark,1990.0,2.0,28.0,1.9081705808639526,0.0,1.4425099260762082,6.800000190734863,27.542861938476562,5.300000190734863,1.0
|
| 77 |
+
Denmark,1991.0,2.0,28.0,2.0105743408203125,0.0,1.0378748173615113,7.199999809265137,21.992252349853516,4.400000095367432,1.0
|
| 78 |
+
Denmark,1992.0,2.0,28.0,2.0145463943481445,0.0,1.638813719999451,7.900000095367432,21.992252349853516,4.400000095367432,1.0
|
| 79 |
+
Denmark,1993.0,2.0,28.0,1.9500609636306763,0.0,-0.421919019070879,8.600000381469727,21.992252349853516,4.400000095367432,1.0
|
| 80 |
+
Denmark,1994.0,2.0,29.0,1.9579061269760132,0.0,5.16963094221637,9.600000381469727,21.992252349853516,4.400000095367432,1.0
|
| 81 |
+
Denmark,1995.0,2.0,29.0,1.9556466341018677,0.0,2.529641017672615,7.699999809265137,28.2803955078125,4.5,1.0
|
| 82 |
+
Denmark,1996.0,2.0,29.0,1.9356881380081177,0.0,2.319437875983516,6.699999809265137,28.2803955078125,4.5,1.0
|
| 83 |
+
Denmark,1997.0,2.0,29.0,1.9083492755889893,0.0,2.832669401412172,6.300000190734863,28.2803955078125,4.5,1.0
|
| 84 |
+
Denmark,1998.0,2.0,30.0,1.94357168674469,1.0,1.8475946395374205,5.199999809265137,28.2803955078125,4.5,1.0
|
| 85 |
+
Denmark,1999.0,2.0,30.0,1.9375298023223877,1.0,2.607938313359242,4.900000095367432,27.80731964111328,4.699999809265137,1.0
|
| 86 |
+
Denmark,2000.0,3.0,30.0,1.9458750486373901,1.0,3.400712375970434,5.199999809265137,27.80731964111328,4.699999809265137,1.0
|
| 87 |
+
Denmark,2001.0,3.0,31.0,1.958469033241272,1.0,0.4625373612847973,4.300000190734863,27.80731964111328,4.699999809265137,1.0
|
| 88 |
+
Denmark,2002.0,3.0,31.0,2.0862817764282227,1.0,0.14583355852825092,4.5,25.756959915161133,4.5,1.0
|
| 89 |
+
Denmark,2003.0,3.0,31.0,2.055290937423706,1.0,0.11739086070816239,4.599999904632568,25.756959915161133,4.5,1.0
|
| 90 |
+
Denmark,2004.0,3.0,31.0,2.027836799621582,1.0,2.3740413211732485,5.400000095367432,25.756959915161133,4.5,1.0
|
| 91 |
+
Denmark,2005.0,3.0,32.0,2.0592081546783447,1.0,2.1552225527034277,5.5,25.756959915161133,4.5,1.0
|
| 92 |
+
Denmark,2006.0,3.0,32.0,2.06137752532959,1.0,3.45617956737963,4.800000190734863,26.37873649597168,4.900000095367432,1.0
|
| 93 |
+
Denmark,2007.0,3.0,33.0,2.0794239044189453,1.0,0.37828541883779127,3.9000000953674316,26.37873649597168,4.900000095367432,1.0
|
| 94 |
+
Denmark,2008.0,3.0,33.0,2.2270941734313965,1.0,-1.2995748662723614,3.799999952316284,16.22600746154785,5.300000190734863,1.0
|
| 95 |
+
Denmark,2009.0,3.0,33.0,2.1570663452148438,1.0,-5.594434350459512,3.4000000953674316,16.22600746154785,5.300000190734863,1.0
|
| 96 |
+
Denmark,2010.0,4.0,33.0,2.164388656616211,1.0,1.1747180750332722,6.0,16.22600746154785,5.300000190734863,1.0
|
| 97 |
+
Denmark,2011.0,4.0,34.0,2.2540342807769775,1.0,0.7365170934216061,7.5,16.22600746154785,5.599999904632568,1.0
|
| 98 |
+
Denmark,2012.0,4.0,34.0,2.3249263763427734,1.0,-0.4486118571335496,7.599999904632568,37.62282943725586,5.599999904632568,1.0
|
| 99 |
+
Denmark,2013.0,4.0,34.0,2.3262453079223633,1.0,-0.6587986128192824,7.5,37.62282943725586,5.599999904632568,1.0
|
| 100 |
+
Denmark,2014.0,4.0,34.0,2.2988474369049072,1.0,0.7497535602002622,7.0,37.62282943725586,5.599999904632568,1.0
|
| 101 |
+
Denmark,2015.0,4.0,34.0,2.3429653644561768,1.0,0.5987356405380448,6.599999904632568,37.62282943725586,5.599999904632568,1.0
|
| 102 |
+
Denmark,2016.0,4.0,35.0,2.2510769367218018,1.0,0.5987356405380448,6.199999809265137,,,1.0
|
| 103 |
+
Finland,1993.0,2.0,36.0,1.9884569644927979,0.0,-1.213633412863158,,16.710485458374023,5.199999809265137,0.0
|
| 104 |
+
Finland,1994.0,2.0,36.0,,0.0,3.4921961319857666,16.299999237060547,16.710485458374023,5.199999809265137,0.0
|
| 105 |
+
Finland,1995.0,2.0,37.0,1.8874015808105469,0.0,3.8101765807171972,16.600000381469727,16.710485458374023,5.199999809265137,0.0
|
| 106 |
+
Finland,1996.0,2.0,37.0,1.903725266456604,0.0,3.319349911091046,15.399999618530273,16.515724182128906,4.800000190734863,0.0
|
| 107 |
+
Finland,1997.0,2.0,37.0,1.930942177772522,0.0,5.936308395875428,14.600000381469727,16.515724182128906,4.800000190734863,0.0
|
| 108 |
+
Finland,1998.0,2.0,37.0,1.8937355279922485,0.0,5.1489995512906646,12.699999809265137,16.515724182128906,4.800000190734863,0.0
|
| 109 |
+
Finland,1999.0,2.0,38.0,2.011321544647217,0.0,4.20198361049188,11.399999618530273,16.515724182128906,4.800000190734863,0.0
|
| 110 |
+
Finland,2000.0,3.0,38.0,1.9423747062683105,0.0,5.415770114432872,10.199999809265137,20.619062423706055,5.099999904632568,0.0
|
| 111 |
+
Finland,2001.0,3.0,38.0,1.9920789003372192,0.0,2.347494292236165,9.800000190734863,20.619062423706055,5.099999904632568,0.0
|
| 112 |
+
Finland,2002.0,3.0,38.0,1.9907097816467285,0.0,1.4341696866532132,9.100000381469727,20.619062423706055,5.099999904632568,0.0
|
| 113 |
+
Finland,2003.0,3.0,39.0,2.0028328895568848,0.0,1.7510617954544887,9.100000381469727,20.619062423706055,5.099999904632568,0.0
|
| 114 |
+
Finland,2004.0,3.0,39.0,1.9357277154922485,0.0,3.6247451347334985,9.0,19.60552215576172,4.900000095367432,0.0
|
| 115 |
+
Finland,2005.0,3.0,39.0,1.94156813621521,0.0,2.4287933454196606,8.800000190734863,19.60552215576172,4.900000095367432,0.0
|
| 116 |
+
Finland,2006.0,3.0,39.0,1.9858746528625488,0.0,3.6566226955136725,8.399999618530273,19.60552215576172,4.900000095367432,0.0
|
| 117 |
+
Finland,2007.0,3.0,40.0,1.9491419792175293,0.0,4.738263811668483,7.699999809265137,19.60552215576172,4.900000095367432,0.0
|
| 118 |
+
Finland,2008.0,3.0,40.0,1.9278813600540161,0.0,0.2528539345030463,6.900000095367432,11.674619674682617,5.099999904632568,0.0
|
| 119 |
+
Finland,2009.0,3.0,40.0,1.8916302919387817,0.0,-8.706689219440781,6.400000095367432,11.674619674682617,5.099999904632568,0.0
|
| 120 |
+
Finland,2010.0,4.0,40.0,1.8865268230438232,0.0,2.522229365649877,8.199999809265137,11.674619674682617,5.099999904632568,0.0
|
| 121 |
+
Finland,2011.0,4.0,41.0,1.8556793928146362,0.0,2.0964421416949497,8.399999618530273,11.674619674682617,5.800000190734863,0.0
|
| 122 |
+
Finland,2012.0,4.0,41.0,1.8262300491333008,0.0,-1.894098840390224,7.800000190734863,17.71783447265625,5.800000190734863,0.0
|
| 123 |
+
Finland,2013.0,4.0,41.0,1.7754852771759033,0.0,-1.2142159432156243,7.699999809265137,17.71783447265625,5.800000190734863,0.0
|
| 124 |
+
Finland,2014.0,4.0,41.0,1.8728125095367432,0.0,-1.1081341442303942,8.199999809265137,17.71783447265625,5.800000190734863,0.0
|
| 125 |
+
Finland,2015.0,4.0,42.0,1.888246774673462,0.0,0.1700447714696051,8.699999809265137,,,0.0
|
| 126 |
+
Finland,2016.0,4.0,42.0,1.8846665620803833,0.0,0.1700447714696051,9.399999618530273,,,0.0
|
| 127 |
+
France,1973.0,0.0,43.0,2.2765939235687256,0.0,5.351538752016531,,32.98605728149414,2.0,0.0
|
| 128 |
+
France,1974.0,0.0,43.0,,0.0,5.351538752016531,2.200000047683716,21.54805564880371,3.5,0.0
|
| 129 |
+
France,1975.0,0.0,43.0,,0.0,5.351538752016531,2.200000047683716,21.54805564880371,3.5,0.0
|
| 130 |
+
France,1976.0,0.0,43.0,2.0915865898132324,0.0,3.761669233757139,3.299999952316284,21.54805564880371,3.5,0.0
|
| 131 |
+
France,1977.0,0.0,43.0,2.2308497428894043,0.0,3.030175211145366,3.5999999046325684,21.54805564880371,3.5,0.0
|
| 132 |
+
France,1978.0,0.0,44.0,2.108224391937256,0.0,3.6315416894043255,4.0,21.54805564880371,3.5,0.0
|
| 133 |
+
France,1979.0,0.0,44.0,2.067662477493286,0.0,3.216074815837822,4.099999904632568,29.40367317199707,4.0,0.0
|
| 134 |
+
France,1980.0,1.0,44.0,2.392162799835205,0.0,1.2023502363484073,4.699999809265137,29.40367317199707,4.0,0.0
|
| 135 |
+
France,1981.0,1.0,45.0,2.0108237266540527,0.0,0.6321581019451529,5.0,29.40367317199707,4.0,0.0
|
| 136 |
+
France,1982.0,1.0,45.0,2.0637447834014893,0.0,2.0077163019131756,6.0,24.196128845214844,2.5,0.0
|
| 137 |
+
France,1983.0,1.0,45.0,2.1103515625,0.0,0.719766674212446,6.599999904632568,24.196128845214844,2.5,0.0
|
| 138 |
+
France,1984.0,1.0,45.0,2.090569257736206,0.0,0.9590809414605852,7.300000190734863,24.196128845214844,2.5,0.0
|
| 139 |
+
France,1985.0,1.0,45.0,2.1482279300689697,0.0,1.041877902951055,8.399999618530273,24.196128845214844,2.5,0.0
|
| 140 |
+
France,1986.0,1.0,46.0,2.0650575160980225,1.0,1.7475094730129883,8.699999809265137,24.196128845214844,2.5,0.0
|
| 141 |
+
France,1987.0,1.0,46.0,2.0313994884490967,1.0,1.9529277164337817,8.899999618530273,24.41234588623047,3.700000047683716,0.0
|
| 142 |
+
France,1988.0,1.0,47.0,2.1799306869506836,1.0,4.093098897502172,8.899999618530273,24.41234588623047,3.700000047683716,0.0
|
| 143 |
+
France,1989.0,1.0,47.0,2.155261993408203,1.0,3.732277540819151,8.5,22.59486961364746,3.0,0.0
|
| 144 |
+
France,1990.0,2.0,47.0,1.9759389162063599,1.0,2.3334097729411827,8.100000381469727,22.59486961364746,3.0,0.0
|
| 145 |
+
France,1991.0,2.0,47.0,1.9990907907485962,1.0,0.9588666393566769,7.900000095367432,22.59486961364746,3.0,0.0
|
| 146 |
+
France,1992.0,2.0,47.0,2.0726869106292725,1.0,1.0957334459767163,8.100000381469727,22.59486961364746,3.0,0.0
|
| 147 |
+
France,1993.0,2.0,48.0,2.161654233932495,1.0,-1.04235721903615,9.0,22.59486961364746,3.0,0.0
|
| 148 |
+
France,1994.0,2.0,48.0,2.1625068187713623,1.0,1.9651250899732804,10.100000381469727,28.730777740478516,2.5999999046325684,0.0
|
| 149 |
+
France,1995.0,2.0,48.0,2.1238434314727783,1.0,1.716969307449081,10.399999618530273,28.730777740478516,2.5999999046325684,0.0
|
| 150 |
+
France,1996.0,2.0,48.0,2.137728452682495,1.0,1.0296469826807575,10.199999809265137,28.730777740478516,2.5999999046325684,0.0
|
| 151 |
+
France,1997.0,2.0,49.0,2.2057838439941406,1.0,1.9759612815486876,10.5,28.730777740478516,2.5999999046325684,0.0
|
| 152 |
+
France,1998.0,2.0,49.0,2.117824077606201,1.0,3.1751824827042587,10.699999809265137,27.51267433166504,3.0999999046325684,0.0
|
| 153 |
+
France,1999.0,2.0,49.0,2.103020191192627,1.0,2.8764910762236546,10.300000190734863,27.51267433166504,3.0999999046325684,0.0
|
| 154 |
+
France,2000.0,3.0,49.0,2.0648281574249268,1.0,3.1661201411272906,10.0,27.51267433166504,3.0999999046325684,0.0
|
| 155 |
+
France,2001.0,3.0,49.0,2.0509839057922363,1.0,1.2151274173554856,8.600000381469727,27.51267433166504,3.0999999046325684,0.0
|
| 156 |
+
France,2002.0,3.0,50.0,2.1570982933044434,1.0,0.38576067553860666,7.800000190734863,27.51267433166504,3.0999999046325684,0.0
|
| 157 |
+
France,2003.0,3.0,50.0,2.115171432495117,1.0,0.10747346969711369,7.900000095367432,17.60466957092285,2.0,0.0
|
| 158 |
+
France,2004.0,3.0,50.0,2.0251121520996094,1.0,2.032366715318132,8.5,17.60466957092285,2.0,0.0
|
| 159 |
+
France,2005.0,3.0,50.0,1.8724911212921143,1.0,0.8446687731688128,8.899999618530273,17.60466957092285,2.0,0.0
|
| 160 |
+
France,2006.0,3.0,50.0,1.8728809356689453,1.0,1.6636800758605774,8.899999618530273,17.60466957092285,2.0,0.0
|
| 161 |
+
France,2007.0,3.0,51.0,2.0379035472869873,1.0,1.7301317098505968,8.800000190734863,17.60466957092285,2.0,0.0
|
| 162 |
+
France,2008.0,3.0,51.0,1.981358289718628,1.0,-0.36309233699641047,8.0,26.011911392211914,2.0,0.0
|
| 163 |
+
France,2009.0,3.0,51.0,1.9445903301239014,1.0,-3.4394122682861137,7.400000095367432,26.011911392211914,2.0,0.0
|
| 164 |
+
France,2010.0,4.0,51.0,1.97519052028656,1.0,1.4631511388479301,9.100000381469727,26.011911392211914,2.0,0.0
|
| 165 |
+
France,2011.0,4.0,51.0,2.0104379653930664,1.0,1.5867201616799245,9.300000190734863,26.011911392211914,2.0,0.0
|
| 166 |
+
France,2012.0,4.0,52.0,2.245067834854126,1.0,-0.301002029280741,9.199999809265137,26.011911392211914,2.0,0.0
|
| 167 |
+
France,2013.0,4.0,52.0,2.140655040740967,1.0,0.10012110050749358,9.800000190734863,16.658611297607422,2.5,0.0
|
| 168 |
+
France,2014.0,4.0,52.0,2.166053533554077,1.0,-0.5303320703296771,10.300000190734863,16.658611297607422,2.5,0.0
|
| 169 |
+
France,2015.0,4.0,52.0,2.231663703918457,1.0,0.6828875076779704,10.300000190734863,16.658611297607422,2.5,0.0
|
| 170 |
+
France,2016.0,4.0,52.0,2.1519434452056885,1.0,0.6828875076779704,10.399999618530273,16.658611297607422,2.5,0.0
|
| 171 |
+
Germany,1973.0,0.0,53.0,2.183532476425171,0.0,4.448017219917399,,20.693185806274414,2.299999952316284,1.0
|
| 172 |
+
Germany,1974.0,0.0,53.0,,0.0,4.448017219917399,0.800000011920929,20.693185806274414,2.299999952316284,1.0
|
| 173 |
+
Germany,1975.0,0.0,53.0,,0.0,4.448017219917399,1.7999999523162842,20.693185806274414,2.299999952316284,1.0
|
| 174 |
+
Germany,1976.0,0.0,54.0,2.003814220428467,0.0,5.400212126885247,3.299999952316284,20.693185806274414,2.299999952316284,1.0
|
| 175 |
+
Germany,1977.0,0.0,54.0,1.9423202276229858,0.0,3.5814370796924746,3.299999952316284,17.71271324157715,2.299999952316284,1.0
|
| 176 |
+
Germany,1978.0,0.0,54.0,2.065620183944702,0.0,3.0981816491967007,3.200000047683716,17.71271324157715,2.299999952316284,1.0
|
| 177 |
+
Germany,1979.0,0.0,54.0,1.8694548606872559,0.0,4.104331334280865,3.0999999046325684,17.71271324157715,2.299999952316284,1.0
|
| 178 |
+
Germany,1980.0,1.0,55.0,1.9015010595321655,0.0,1.1986939310568603,2.700000047683716,17.71271324157715,2.299999952316284,1.0
|
| 179 |
+
Germany,1981.0,1.0,55.0,1.9462664127349854,0.0,0.3762425136606391,2.700000047683716,21.235219955444336,2.4000000953674316,1.0
|
| 180 |
+
Germany,1982.0,1.0,55.0,2.062150001525879,0.0,-0.3000577871680881,3.9000000953674316,21.235219955444336,2.4000000953674316,1.0
|
| 181 |
+
Germany,1983.0,1.0,56.0,1.9624204635620117,0.0,1.839034163459564,5.599999904632568,21.235219955444336,2.4000000953674316,1.0
|
| 182 |
+
Germany,1984.0,1.0,56.0,1.9374024868011475,0.0,3.1789872184044716,6.900000095367432,22.778295516967773,2.5,1.0
|
| 183 |
+
Germany,1985.0,1.0,56.0,2.0254576206207275,0.0,2.5568835645230026,7.099999904632568,22.778295516967773,2.5,1.0
|
| 184 |
+
Germany,1986.0,1.0,56.0,2.0180559158325195,0.0,2.24053504560923,7.199999809265137,22.778295516967773,2.5,1.0
|
| 185 |
+
Germany,1987.0,1.0,57.0,1.9818177223205566,0.0,1.2464996325366084,6.599999904632568,22.778295516967773,2.5,1.0
|
| 186 |
+
Germany,1988.0,1.0,57.0,2.0866997241973877,0.0,3.302863731086273,6.400000095367432,17.1455020904541,2.799999952316284,1.0
|
| 187 |
+
Germany,1989.0,1.0,57.0,2.0898427963256836,0.0,3.096180031770962,6.300000190734863,17.1455020904541,2.799999952316284,1.0
|
| 188 |
+
Germany,1990.0,2.0,58.0,1.9681951999664307,0.0,4.351639057513319,5.599999904632568,17.1455020904541,2.799999952316284,1.0
|
| 189 |
+
Germany,1991.0,2.0,58.0,1.9706921577453613,0.0,4.3452200415312765,4.800000190734863,14.377721786499023,2.5999999046325684,1.0
|
| 190 |
+
Germany,1992.0,2.0,58.0,1.9044424295425415,0.0,1.1517712511661966,5.599999904632568,14.377721786499023,2.5999999046325684,1.0
|
| 191 |
+
Germany,1993.0,2.0,58.0,1.8491579294204712,0.0,-1.6051353052851056,6.599999904632568,14.377721786499023,2.5999999046325684,1.0
|
| 192 |
+
Germany,1994.0,2.0,59.0,1.8414329290390015,0.0,2.1026072317977245,7.800000190734863,14.377721786499023,2.5999999046325684,1.0
|
| 193 |
+
Germany,1995.0,2.0,59.0,1.798919439315796,0.0,1.4390681582731037,8.399999618530273,22.138059616088867,2.9000000953674316,1.0
|
| 194 |
+
Germany,1996.0,2.0,59.0,1.8086233139038086,0.0,0.5264771073661573,8.199999809265137,22.138059616088867,2.9000000953674316,1.0
|
| 195 |
+
Germany,1997.0,2.0,59.0,1.8051198720932007,0.0,1.7002907261413553,8.899999618530273,22.138059616088867,2.9000000953674316,1.0
|
| 196 |
+
Germany,1998.0,2.0,60.0,1.8164921998977661,0.0,1.9641761389726127,9.600000381469727,22.138059616088867,2.9000000953674316,1.0
|
| 197 |
+
Germany,1999.0,2.0,60.0,1.9864951372146606,0.0,1.92123729372423,9.399999618530273,21.303415298461914,2.9000000953674316,1.0
|
| 198 |
+
Germany,2000.0,3.0,60.0,1.8792535066604614,0.0,2.822696604435391,8.600000381469727,21.303415298461914,2.9000000953674316,1.0
|
| 199 |
+
Germany,2001.0,3.0,60.0,1.786348581314087,0.0,1.524537694849327,7.900000095367432,21.303415298461914,2.9000000953674316,1.0
|
| 200 |
+
Germany,2002.0,3.0,61.0,1.8350909948349,0.0,-0.16798706292313767,7.800000190734863,21.303415298461914,2.9000000953674316,1.0
|
| 201 |
+
Germany,2003.0,3.0,61.0,1.8083136081695557,0.0,-0.764861234086549,8.600000381469727,16.57202911376953,2.799999952316284,1.0
|
| 202 |
+
Germany,2004.0,3.0,61.0,1.7884893417358398,0.0,1.1919365222102452,9.699999809265137,16.57202911376953,2.799999952316284,1.0
|
| 203 |
+
Germany,2005.0,3.0,62.0,1.8012874126434326,0.0,0.7639097056733406,10.399999618530273,16.57202911376953,2.799999952316284,1.0
|
| 204 |
+
Germany,2006.0,3.0,62.0,1.732218861579895,0.0,3.8171967522014505,11.199999809265137,22.534643173217773,3.4000000953674316,1.0
|
| 205 |
+
Germany,2007.0,3.0,62.0,1.5918943881988525,0.0,3.3987061701518395,10.100000381469727,22.534643173217773,3.4000000953674316,1.0
|
| 206 |
+
Germany,2008.0,3.0,62.0,1.689921259880066,0.0,1.2746990378069987,8.5,22.534643173217773,3.4000000953674316,1.0
|
| 207 |
+
Germany,2009.0,3.0,63.0,1.6629904508590698,0.0,-5.379411050281347,7.400000095367432,22.534643173217773,3.4000000953674316,1.0
|
| 208 |
+
Germany,2010.0,4.0,63.0,1.6525452136993408,0.0,4.239504345235163,7.599999904632568,14.473562240600586,4.0,1.0
|
| 209 |
+
Germany,2011.0,4.0,63.0,1.6600449085235596,0.0,3.6337131067081936,7.0,14.473562240600586,4.0,1.0
|
| 210 |
+
Germany,2012.0,4.0,63.0,1.5492587089538574,0.0,2.11781505045475,5.800000190734863,14.473562240600586,4.0,1.0
|
| 211 |
+
Germany,2013.0,4.0,64.0,1.5601043701171875,0.0,-1.7865998329556119,5.400000095367432,14.473562240600586,4.0,1.0
|
| 212 |
+
Germany,2014.0,4.0,64.0,1.6657062768936157,0.0,3.0428653905437995,5.199999809265137,16.673322677612305,2.799999952316284,1.0
|
| 213 |
+
Germany,2015.0,4.0,64.0,1.633184552192688,0.0,1.1498338641947434,5.0,16.673322677612305,2.799999952316284,1.0
|
| 214 |
+
Germany,2016.0,4.0,64.0,1.632490873336792,0.0,1.1498338641947434,4.599999904632568,16.673322677612305,2.799999952316284,1.0
|
| 215 |
+
Greece,1980.0,1.0,65.0,2.2575790882110596,0.0,-0.30689929454556375,,24.452898025512695,2.299999952316284,1.0
|
| 216 |
+
Greece,1981.0,1.0,66.0,2.4391214847564697,0.0,-2.4324610502786577,2.700000047683716,24.452898025512695,2.299999952316284,1.0
|
| 217 |
+
Greece,1982.0,1.0,66.0,2.3068349361419678,0.0,-1.7402524455702562,4.0,25.9151668548584,2.0999999046325684,1.0
|
| 218 |
+
Greece,1983.0,1.0,66.0,2.384683847427368,0.0,-1.6524021701516158,5.800000190734863,25.9151668548584,2.0999999046325684,1.0
|
| 219 |
+
Greece,1984.0,1.0,66.0,2.1335229873657227,0.0,1.5036716319182153,7.099999904632568,25.9151668548584,2.0999999046325684,1.0
|
| 220 |
+
Greece,1985.0,1.0,67.0,2.2406749725341797,0.0,2.112295008275422,7.199999809265137,25.9151668548584,2.0999999046325684,1.0
|
| 221 |
+
Greece,1986.0,1.0,67.0,2.3674230575561523,0.0,0.18573805517520772,7.0,18.60298728942871,2.0999999046325684,1.0
|
| 222 |
+
Greece,1987.0,1.0,67.0,2.218081474304199,0.0,-2.585123575947364,6.599999904632568,18.60298728942871,2.0999999046325684,1.0
|
| 223 |
+
Greece,1988.0,1.0,67.0,2.37579345703125,0.0,3.909777308430187,6.699999809265137,18.60298728942871,2.0999999046325684,1.0
|
| 224 |
+
Greece,1989.0,1.0,68.0,2.4371073246002197,0.0,3.2597296084599146,6.800000190734863,18.60298728942871,2.0999999046325684,1.0
|
| 225 |
+
Greece,1990.0,2.0,69.0,2.3199336528778076,0.0,-1.0522328983468903,6.699999809265137,28.869836807250977,2.3499999046325684,1.0
|
| 226 |
+
Greece,1991.0,2.0,69.0,2.352632522583008,0.0,1.8698344489763594,6.400000095367432,31.866207122802734,2.200000047683716,1.0
|
| 227 |
+
Greece,1992.0,2.0,69.0,2.340679168701172,0.0,-0.06629934926419984,7.099999904632568,31.866207122802734,2.200000047683716,1.0
|
| 228 |
+
Greece,1993.0,2.0,70.0,2.157702922821045,0.0,-2.177150488469816,7.900000095367432,31.866207122802734,2.200000047683716,1.0
|
| 229 |
+
Greece,1994.0,2.0,70.0,2.2957708835601807,0.0,1.4905589586424748,8.600000381469727,22.60158348083496,2.200000047683716,1.0
|
| 230 |
+
Greece,1995.0,2.0,70.0,2.2564210891723633,0.0,1.6238251927022698,8.899999618530273,22.60158348083496,2.200000047683716,1.0
|
| 231 |
+
Greece,1996.0,2.0,71.0,2.275092124938965,0.0,2.4098430342685466,9.199999809265137,22.60158348083496,2.200000047683716,1.0
|
| 232 |
+
Greece,1997.0,2.0,71.0,2.2537894248962402,0.0,3.970081949817163,9.600000381469727,16.300907135009766,2.4000000953674316,1.0
|
| 233 |
+
Greece,1998.0,2.0,71.0,2.111912727355957,0.0,3.320699470490025,9.800000190734863,16.300907135009766,2.4000000953674316,1.0
|
| 234 |
+
Greece,1999.0,2.0,71.0,2.0891411304473877,0.0,2.6780997003152156,11.100000381469727,16.300907135009766,2.4000000953674316,1.0
|
| 235 |
+
Greece,2000.0,3.0,72.0,2.3298847675323486,0.0,3.495563615194644,12.0,16.300907135009766,2.4000000953674316,1.0
|
| 236 |
+
Greece,2001.0,3.0,72.0,2.231703519821167,0.0,3.591652801512423,11.199999809265137,12.367311477661133,2.200000047683716,1.0
|
| 237 |
+
Greece,2002.0,3.0,72.0,2.227895736694336,0.0,3.542622806126142,10.699999809265137,12.367311477661133,2.200000047683716,1.0
|
| 238 |
+
Greece,2003.0,3.0,72.0,2.09820818901062,0.0,5.542360849137185,10.300000190734863,12.367311477661133,2.200000047683716,1.0
|
| 239 |
+
Greece,2004.0,3.0,73.0,2.2174253463745117,0.0,4.8013787332707345,9.699999809265137,12.367311477661133,2.200000047683716,1.0
|
| 240 |
+
Greece,2005.0,3.0,73.0,2.2797317504882812,0.0,0.30456813164250557,10.600000381469727,13.133931159973145,2.200000047683716,1.0
|
| 241 |
+
Greece,2006.0,3.0,73.0,2.261026620864868,0.0,5.335601874743761,10.0,13.133931159973145,2.200000047683716,1.0
|
| 242 |
+
Greece,2007.0,3.0,74.0,2.3413443565368652,1.0,3.0109840030980797,9.0,13.133931159973145,2.200000047683716,1.0
|
| 243 |
+
Greece,2008.0,3.0,74.0,2.420492172241211,1.0,-0.5993898043673805,8.399999618530273,23.639280319213867,2.5999999046325684,1.0
|
| 244 |
+
Greece,2009.0,3.0,75.0,2.1895577907562256,1.0,-4.552117255447617,7.800000190734863,23.639280319213867,2.5999999046325684,1.0
|
| 245 |
+
Greece,2010.0,4.0,75.0,1.9221687316894531,1.0,-5.600777666961475,9.600000381469727,42.342525482177734,2.5999999046325684,1.0
|
| 246 |
+
Greece,2011.0,4.0,75.0,1.9290088415145874,1.0,-8.99795501594073,12.699999809265137,42.342525482177734,2.5999999046325684,1.0
|
| 247 |
+
Greece,2012.0,4.0,76.0,2.211064100265503,1.0,-6.797860844308075,17.899999618530273,42.342525482177734,2.5999999046325684,1.0
|
| 248 |
+
Greece,2013.0,4.0,76.0,2.174196720123291,1.0,-2.4937588484097413,24.5,23.831594467163086,4.333333492279053,1.0
|
| 249 |
+
Greece,2014.0,4.0,76.0,2.1439359188079834,1.0,1.3266902234945075,27.5,23.831594467163086,4.333333492279053,1.0
|
| 250 |
+
Greece,2015.0,4.0,77.0,2.0541112422943115,1.0,0.40149990137542835,26.5,23.831594467163086,3.1588234901428223,1.0
|
| 251 |
+
Greece,2016.0,4.0,78.0,1.9624214172363281,1.0,0.40149990137542835,24.899999618530273,27.232513427734375,,1.0
|
| 252 |
+
Italy,1973.0,0.0,79.0,2.2886667251586914,0.0,6.401805943527498,,14.307647705078125,3.5,1.0
|
| 253 |
+
Italy,1974.0,0.0,79.0,,0.0,6.401805943527498,5.900000095367432,14.307647705078125,3.5,1.0
|
| 254 |
+
Italy,1975.0,0.0,79.0,,0.0,6.401805943527498,5.0,14.307647705078125,3.5,1.0
|
| 255 |
+
Italy,1976.0,0.0,80.0,2.3443145751953125,0.0,6.592319755489807,5.5,14.307647705078125,3.5,1.0
|
| 256 |
+
Italy,1977.0,0.0,80.0,2.219820976257324,0.0,2.1258274597249542,6.199999809265137,13.635860443115234,3.0999999046325684,1.0
|
| 257 |
+
Italy,1978.0,0.0,80.0,2.1708672046661377,0.0,2.872936388140965,6.699999809265137,13.635860443115234,3.0999999046325684,1.0
|
| 258 |
+
Italy,1979.0,0.0,81.0,2.1693036556243896,0.0,5.653224014988707,6.699999809265137,13.635860443115234,3.0999999046325684,1.0
|
| 259 |
+
Italy,1980.0,1.0,81.0,2.085391044616699,0.0,3.217170111744038,7.199999809265137,14.758296012878418,3.4000000953674316,1.0
|
| 260 |
+
Italy,1981.0,1.0,81.0,2.1697657108306885,0.0,0.7232324152947273,7.099999904632568,14.758296012878418,3.4000000953674316,1.0
|
| 261 |
+
Italy,1982.0,1.0,81.0,2.1470797061920166,0.0,0.33922496320027556,7.400000095367432,14.758296012878418,3.4000000953674316,1.0
|
| 262 |
+
Italy,1983.0,1.0,82.0,2.1497642993927,0.0,1.1324911594419964,8.0,14.758296012878418,3.4000000953674316,1.0
|
| 263 |
+
Italy,1984.0,1.0,82.0,2.17533016204834,0.0,3.202783043112286,7.400000095367432,14.471368789672852,4.0,1.0
|
| 264 |
+
Italy,1985.0,1.0,82.0,2.1354143619537354,0.0,2.768381433594863,7.900000095367432,14.471368789672852,4.0,1.0
|
| 265 |
+
Italy,1986.0,1.0,82.0,2.2362987995147705,0.0,2.854366907212367,8.199999809265137,14.471368789672852,4.0,1.0
|
| 266 |
+
Italy,1987.0,1.0,83.0,2.106578826904297,0.0,3.181430295190417,8.899999618530273,14.471368789672852,4.0,1.0
|
| 267 |
+
Italy,1988.0,1.0,83.0,2.1211605072021484,0.0,4.144042085640591,9.600000381469727,26.127628326416016,4.0,1.0
|
| 268 |
+
Italy,1989.0,1.0,83.0,2.1933279037475586,0.0,3.310861987806209,9.699999809265137,26.127628326416016,4.0,1.0
|
| 269 |
+
Italy,1990.0,2.0,83.0,2.0940353870391846,0.0,1.9004397931400836,9.699999809265137,26.127628326416016,4.0,1.0
|
| 270 |
+
Italy,1991.0,2.0,83.0,2.103262424468994,0.0,1.4681756016925849,8.899999618530273,26.127628326416016,4.0,1.0
|
| 271 |
+
Italy,1992.0,2.0,84.0,2.0569980144500732,1.0,0.7658076167858774,8.5,26.127628326416016,4.0,1.0
|
| 272 |
+
Italy,1993.0,2.0,84.0,2.056490898132324,1.0,-0.9134017164166668,8.800000190734863,16.623777389526367,5.599999904632568,1.0
|
| 273 |
+
Italy,1994.0,2.0,85.0,2.333101511001587,1.0,2.130215483696909,9.699999809265137,16.623777389526367,5.599999904632568,1.0
|
| 274 |
+
Italy,1995.0,2.0,85.0,2.3677167892456055,1.0,2.88520233329654,10.600000381469727,20.197978973388672,6.5,1.0
|
| 275 |
+
Italy,1996.0,2.0,86.0,2.3810524940490723,1.0,1.2579045029956717,11.199999809265137,20.197978973388672,6.5,1.0
|
| 276 |
+
Italy,1997.0,2.0,86.0,2.3347463607788086,1.0,1.7815015766421174,11.199999809265137,24.022483825683594,5.900000095367432,1.0
|
| 277 |
+
Italy,1998.0,2.0,86.0,2.3303332328796387,1.0,1.5868383679661922,11.199999809265137,24.022483825683594,5.900000095367432,1.0
|
| 278 |
+
Italy,1999.0,2.0,86.0,2.3052444458007812,1.0,1.5427691071818928,11.300000190734863,24.022483825683594,5.900000095367432,1.0
|
| 279 |
+
Italy,2000.0,3.0,86.0,2.3637232780456543,1.0,3.6631341831943995,10.899999618530273,24.022483825683594,5.900000095367432,1.0
|
| 280 |
+
Italy,2001.0,3.0,87.0,2.4273459911346436,1.0,1.7150390245860305,10.0,24.022483825683594,5.900000095367432,1.0
|
| 281 |
+
Italy,2002.0,3.0,87.0,2.442051887512207,1.0,0.09937085163839801,9.0,16.647994995117188,5.199999809265137,1.0
|
| 282 |
+
Italy,2003.0,3.0,87.0,2.384060859680176,1.0,-0.29286809921718715,8.5,16.647994995117188,5.199999809265137,1.0
|
| 283 |
+
Italy,2004.0,3.0,87.0,2.451977252960205,1.0,0.9266380795793319,8.399999618530273,16.647994995117188,5.199999809265137,1.0
|
| 284 |
+
Italy,2005.0,3.0,87.0,2.461768388748169,1.0,0.45482596813098736,8.0,16.647994995117188,5.199999809265137,1.0
|
| 285 |
+
Italy,2006.0,3.0,88.0,2.6511244773864746,1.0,1.7004572304054748,7.699999809265137,16.647994995117188,5.199999809265137,1.0
|
| 286 |
+
Italy,2007.0,3.0,88.0,2.474781036376953,1.0,0.9627871465950959,6.800000190734863,32.95000076293945,5.099999904632568,1.0
|
| 287 |
+
Italy,2008.0,3.0,89.0,2.529945135116577,1.0,-1.7037494337179746,6.099999904632568,32.95000076293945,5.099999904632568,1.0
|
| 288 |
+
Italy,2009.0,3.0,89.0,2.435713768005371,1.0,-5.9117109008596564,6.699999809265137,10.544108390808105,3.0,1.0
|
| 289 |
+
Italy,2010.0,4.0,89.0,2.3694911003112793,1.0,1.3742250063839612,7.699999809265137,10.544108390808105,3.0,1.0
|
| 290 |
+
Italy,2011.0,4.0,89.0,2.3103575706481934,1.0,0.4038033503490383,8.399999618530273,10.544108390808105,3.0,1.0
|
| 291 |
+
Italy,2012.0,4.0,89.0,2.3110239505767822,1.0,-3.0806092426210347,8.399999618530273,10.544108390808105,3.0,1.0
|
| 292 |
+
Italy,2013.0,4.0,90.0,2.4591422080993652,1.0,-2.8805992265559865,10.699999809265137,10.544108390808105,3.0,1.0
|
| 293 |
+
Italy,2014.0,4.0,90.0,2.209761619567871,1.0,-1.2533345915000649,12.100000381469727,21.700159072875977,3.5,1.0
|
| 294 |
+
Italy,2015.0,4.0,90.0,2.2426037788391113,1.0,0.738080885053049,12.699999809265137,21.700159072875977,3.5,1.0
|
| 295 |
+
Italy,2016.0,4.0,90.0,2.192692995071411,1.0,0.738080885053049,11.899999618530273,21.700159072875977,3.5,1.0
|
| 296 |
+
Luxembourg,1973.0,0.0,91.0,1.9828577041625977,0.0,7.122767886545565,,13.429516792297363,3.4000000953674316,0.0
|
| 297 |
+
Luxembourg,1974.0,0.0,92.0,,0.0,7.122767886545565,0.0,13.429516792297363,3.4000000953674316,0.0
|
| 298 |
+
Luxembourg,1975.0,0.0,92.0,,0.0,7.122767886545565,0.0,13.662456512451172,3.5,0.0
|
| 299 |
+
Luxembourg,1976.0,0.0,92.0,2.0314218997955322,0.0,2.028099037013566,0.20000000298023224,13.662456512451172,3.5,0.0
|
| 300 |
+
Luxembourg,1977.0,0.0,92.0,2.1257567405700684,0.0,1.3935411391699892,0.30000001192092896,13.662456512451172,3.5,0.0
|
| 301 |
+
Luxembourg,1978.0,0.0,92.0,2.0027003288269043,0.0,3.8883873657756447,0.5,13.662456512451172,3.5,0.0
|
| 302 |
+
Luxembourg,1979.0,0.0,93.0,2.068141460418701,0.0,2.1061437182057445,1.2000000476837158,13.662456512451172,3.5,0.0
|
| 303 |
+
Luxembourg,1980.0,1.0,93.0,1.912745475769043,0.0,0.4825466111465238,2.4000000953674316,29.38443374633789,3.0,0.0
|
| 304 |
+
Luxembourg,1981.0,1.0,93.0,2.0261082649230957,0.0,-0.8436682741634791,2.4000000953674316,29.38443374633789,3.0,0.0
|
| 305 |
+
Luxembourg,1982.0,1.0,93.0,1.9981039762496948,0.0,1.048294920421316,2.4000000953674316,29.38443374633789,3.0,0.0
|
| 306 |
+
Luxembourg,1983.0,1.0,93.0,2.087477445602417,0.0,2.961753540450444,2.4000000953674316,29.38443374633789,3.0,0.0
|
| 307 |
+
Luxembourg,1984.0,1.0,94.0,2.0282585620880127,0.0,6.07785050717666,3.4000000953674316,29.38443374633789,3.0,0.0
|
| 308 |
+
Luxembourg,1985.0,1.0,94.0,2.1583499908447266,0.0,2.5932461469376498,3.0,21.627099990844727,3.200000047683716,0.0
|
| 309 |
+
Luxembourg,1986.0,1.0,94.0,1.9987359046936035,0.0,9.491574169845345,2.9000000953674316,21.627099990844727,3.200000047683716,0.0
|
| 310 |
+
Luxembourg,1987.0,1.0,94.0,2.0876684188842773,0.0,3.279467714596478,2.5999999046325684,21.627099990844727,3.200000047683716,0.0
|
| 311 |
+
Luxembourg,1988.0,1.0,94.0,2.2340970039367676,0.0,7.6791058793378655,2.5,21.627099990844727,3.200000047683716,0.0
|
| 312 |
+
Luxembourg,1989.0,1.0,95.0,2.1052918434143066,0.0,8.73531168517369,2.0,21.627099990844727,3.200000047683716,0.0
|
| 313 |
+
Luxembourg,1990.0,2.0,95.0,1.887711524963379,0.0,4.009810792564297,1.7999999523162842,15.711371421813965,3.799999952316284,0.0
|
| 314 |
+
Luxembourg,1991.0,2.0,95.0,2.082866668701172,0.0,7.198409111596777,1.7000000476837158,15.711371421813965,3.799999952316284,0.0
|
| 315 |
+
Luxembourg,1992.0,2.0,95.0,1.7342784404754639,0.0,0.4760768707152388,1.600000023841858,15.711371421813965,3.799999952316284,0.0
|
| 316 |
+
Luxembourg,1993.0,2.0,95.0,1.754077672958374,0.0,2.811218794749865,2.0999999046325684,15.711371421813965,3.799999952316284,0.0
|
| 317 |
+
Luxembourg,1994.0,2.0,96.0,1.7493903636932373,0.0,2.416630027102083,2.5999999046325684,15.711371421813965,3.799999952316284,0.0
|
| 318 |
+
Luxembourg,1995.0,2.0,96.0,1.8338433504104614,0.0,0.01730034936551306,3.200000047683716,15.318608283996582,3.9000000953674316,0.0
|
| 319 |
+
Luxembourg,1996.0,2.0,96.0,1.7339081764221191,0.0,0.1427453404826976,2.9000000953674316,15.318608283996582,3.9000000953674316,0.0
|
| 320 |
+
Luxembourg,1997.0,2.0,96.0,2.0083518028259277,0.0,4.61812653248192,2.9000000953674316,15.318608283996582,3.9000000953674316,0.0
|
| 321 |
+
Luxembourg,1998.0,2.0,96.0,1.795021653175354,0.0,5.1748943749517045,2.700000047683716,15.318608283996582,3.9000000953674316,0.0
|
| 322 |
+
Luxembourg,1999.0,2.0,97.0,1.8373445272445679,0.0,6.965856606196974,2.700000047683716,15.318608283996582,3.9000000953674316,0.0
|
| 323 |
+
Luxembourg,2000.0,3.0,97.0,1.8770883083343506,0.0,6.994055448559958,2.4000000953674316,9.475189208984375,4.199999809265137,0.0
|
| 324 |
+
Luxembourg,2001.0,3.0,97.0,1.8127585649490356,0.0,0.9439800708337651,2.200000047683716,9.475189208984375,4.199999809265137,0.0
|
| 325 |
+
Luxembourg,2002.0,3.0,97.0,1.7919378280639648,0.0,2.5408548135264923,1.899999976158142,9.475189208984375,4.199999809265137,0.0
|
| 326 |
+
Luxembourg,2003.0,3.0,97.0,1.9327468872070312,0.0,0.17291407333371625,2.5999999046325684,9.475189208984375,4.199999809265137,0.0
|
| 327 |
+
Luxembourg,2004.0,3.0,98.0,2.000948905944824,0.0,2.9375844621727265,3.799999952316284,9.475189208984375,4.199999809265137,0.0
|
| 328 |
+
Luxembourg,2005.0,3.0,98.0,1.985403299331665,0.0,1.6491855255835792,5.0,11.147483825683594,3.799999952316284,0.0
|
| 329 |
+
Luxembourg,2006.0,3.0,98.0,1.9116462469100952,0.0,3.4473264480511445,4.599999904632568,11.147483825683594,3.799999952316284,0.0
|
| 330 |
+
Luxembourg,2007.0,3.0,98.0,1.8530555963516235,0.0,6.7344596661045735,4.599999904632568,11.147483825683594,3.799999952316284,0.0
|
| 331 |
+
Luxembourg,2008.0,3.0,98.0,1.9941309690475464,0.0,-2.599799641528532,4.199999809265137,11.147483825683594,3.799999952316284,0.0
|
| 332 |
+
Luxembourg,2009.0,3.0,99.0,2.0941529273986816,0.0,-7.113070504163647,4.900000095367432,11.147483825683594,3.799999952316284,0.0
|
| 333 |
+
Luxembourg,2010.0,4.0,99.0,1.943558692932129,0.0,3.7651280834781247,5.099999904632568,17.068668365478516,3.5999999046325684,0.0
|
| 334 |
+
Luxembourg,2011.0,4.0,99.0,1.8463540077209473,0.0,0.31114665017135124,4.599999904632568,17.068668365478516,3.5999999046325684,0.0
|
| 335 |
+
Luxembourg,2012.0,4.0,99.0,1.9027764797210693,0.0,-3.1999068469804355,4.800000190734863,17.068668365478516,3.5999999046325684,0.0
|
| 336 |
+
Luxembourg,2013.0,4.0,100.0,1.819449782371521,0.0,1.962698971922572,5.099999904632568,17.068668365478516,3.5999999046325684,0.0
|
| 337 |
+
Luxembourg,2014.0,4.0,100.0,1.8999555110931396,0.0,1.6450835789246396,5.900000095367432,14.434012413024902,3.9000000953674316,0.0
|
| 338 |
+
Luxembourg,2015.0,4.0,100.0,1.7389495372772217,0.0,2.3908688043664243,6.0,14.434012413024902,3.9000000953674316,0.0
|
| 339 |
+
Luxembourg,2016.0,4.0,100.0,1.747226595878601,0.0,2.3908688043664243,6.5,14.434012413024902,3.9000000953674316,0.0
|
| 340 |
+
Netherlands,1973.0,0.0,101.0,2.5651395320892334,0.0,4.575449145360767,,22.861536026000977,5.099999904632568,1.0
|
| 341 |
+
Netherlands,1974.0,0.0,101.0,,0.0,4.575449145360767,2.4000000953674316,22.861536026000977,5.099999904632568,1.0
|
| 342 |
+
Netherlands,1975.0,0.0,101.0,,0.0,4.575449145360767,2.9000000953674316,22.861536026000977,5.099999904632568,1.0
|
| 343 |
+
Netherlands,1976.0,0.0,101.0,2.3875246047973633,0.0,3.6395444485196413,5.5,22.861536026000977,5.099999904632568,1.0
|
| 344 |
+
Netherlands,1977.0,0.0,102.0,2.5166068077087402,0.0,1.912636145854419,5.800000190734863,22.861536026000977,5.099999904632568,1.0
|
| 345 |
+
Netherlands,1978.0,0.0,102.0,2.544010639190674,0.0,2.066332933776651,5.599999904632568,22.004241943359375,3.299999952316284,1.0
|
| 346 |
+
Netherlands,1979.0,0.0,102.0,2.3571131229400635,0.0,1.3124053574646068,5.599999904632568,22.004241943359375,3.299999952316284,1.0
|
| 347 |
+
Netherlands,1980.0,1.0,102.0,2.2565505504608154,0.0,0.5431280821706975,5.699999809265137,22.004241943359375,3.299999952316284,1.0
|
| 348 |
+
Netherlands,1981.0,1.0,103.0,2.371997117996216,0.0,-1.4619529179052217,6.400000095367432,22.004241943359375,3.299999952316284,1.0
|
| 349 |
+
Netherlands,1982.0,1.0,104.0,2.3064496517181396,0.0,-1.692590271112268,8.899999618530273,25.97228240966797,3.799999952316284,1.0
|
| 350 |
+
Netherlands,1983.0,1.0,104.0,2.2235817909240723,0.0,1.6836187331318866,11.800000190734863,22.816944122314453,3.799999952316284,1.0
|
| 351 |
+
Netherlands,1984.0,1.0,104.0,2.1886703968048096,0.0,2.653403829556107,9.5,22.816944122314453,3.799999952316284,1.0
|
| 352 |
+
Netherlands,1985.0,1.0,104.0,2.2042906284332275,0.0,2.1029348514067454,9.300000190734863,22.816944122314453,3.799999952316284,1.0
|
| 353 |
+
Netherlands,1986.0,1.0,105.0,2.24593186378479,0.0,2.2181314883815197,8.399999618530273,22.816944122314453,3.799999952316284,1.0
|
| 354 |
+
Netherlands,1987.0,1.0,105.0,2.0416345596313477,0.0,1.2865150265274226,7.599999904632568,18.79204750061035,3.4000000953674316,1.0
|
| 355 |
+
Netherlands,1988.0,1.0,105.0,2.151905059814453,0.0,2.775043448419906,7.5,18.79204750061035,3.4000000953674316,1.0
|
| 356 |
+
Netherlands,1989.0,1.0,106.0,2.144594192504883,0.0,3.7955694452612327,7.400000095367432,18.79204750061035,3.4000000953674316,1.0
|
| 357 |
+
Netherlands,1990.0,2.0,106.0,2.009793758392334,0.0,3.4682775384371456,6.699999809265137,19.25058937072754,3.799999952316284,1.0
|
| 358 |
+
Netherlands,1991.0,2.0,106.0,2.018831729888916,0.0,1.6350546960639656,6.099999904632568,19.25058937072754,3.799999952316284,1.0
|
| 359 |
+
Netherlands,1992.0,2.0,106.0,1.9931951761245728,0.0,0.9400150226198394,5.699999809265137,19.25058937072754,3.799999952316284,1.0
|
| 360 |
+
Netherlands,1993.0,2.0,106.0,1.9242454767227173,0.0,0.554249674478516,5.699999809265137,19.25058937072754,3.799999952316284,1.0
|
| 361 |
+
Netherlands,1994.0,2.0,107.0,1.9433072805404663,0.0,2.3421680993460385,6.5,19.25058937072754,3.799999952316284,1.0
|
| 362 |
+
Netherlands,1995.0,2.0,107.0,1.8205333948135376,0.0,2.607973403815784,7.199999809265137,15.840326309204102,5.400000095367432,1.0
|
| 363 |
+
Netherlands,1996.0,2.0,107.0,1.8116310834884644,0.0,3.0899674990521713,8.300000190734863,15.840326309204102,5.400000095367432,1.0
|
| 364 |
+
Netherlands,1997.0,2.0,107.0,1.8276861906051636,0.0,3.764345655308596,7.699999809265137,15.840326309204102,5.400000095367432,1.0
|
| 365 |
+
Netherlands,1998.0,2.0,108.0,1.7601391077041626,0.0,3.8828521544296692,6.5,15.840326309204102,5.400000095367432,1.0
|
| 366 |
+
Netherlands,1999.0,2.0,108.0,1.801896572113037,0.0,4.354902624485015,5.099999904632568,19.989351272583008,4.800000190734863,1.0
|
| 367 |
+
Netherlands,2000.0,3.0,108.0,1.8609392642974854,0.0,3.496351712887847,4.199999809265137,19.989351272583008,4.800000190734863,1.0
|
| 368 |
+
Netherlands,2001.0,3.0,108.0,1.7963284254074097,0.0,1.3562916182738416,3.700000047683716,19.989351272583008,4.800000190734863,1.0
|
| 369 |
+
Netherlands,2002.0,3.0,109.0,1.850503921508789,1.0,-0.5332882862158441,3.0999999046325684,19.989351272583008,4.800000190734863,1.0
|
| 370 |
+
Netherlands,2003.0,3.0,110.0,1.9397225379943848,1.0,-0.18811695444312368,3.700000047683716,13.27031135559082,5.800000190734863,1.0
|
| 371 |
+
Netherlands,2004.0,3.0,110.0,1.8879002332687378,1.0,1.6767899400222082,4.800000190734863,17.299602508544922,4.599999904632568,1.0
|
| 372 |
+
Netherlands,2005.0,3.0,110.0,1.8626594543457031,1.0,1.9219335473977974,5.699999809265137,17.299602508544922,4.599999904632568,1.0
|
| 373 |
+
Netherlands,2006.0,3.0,111.0,1.9523825645446777,1.0,3.352505331345188,5.900000095367432,17.299602508544922,4.599999904632568,1.0
|
| 374 |
+
Netherlands,2007.0,3.0,111.0,1.945645809173584,1.0,3.4731516772739695,5.0,16.398202896118164,5.5,1.0
|
| 375 |
+
Netherlands,2008.0,3.0,111.0,1.9566904306411743,1.0,1.3039236197165518,4.199999809265137,16.398202896118164,5.5,1.0
|
| 376 |
+
Netherlands,2009.0,3.0,111.0,1.935982346534729,1.0,-4.261221541748224,3.700000047683716,16.398202896118164,5.5,1.0
|
| 377 |
+
Netherlands,2010.0,4.0,112.0,2.0640711784362793,1.0,0.883876122487889,4.400000095367432,16.398202896118164,5.5,1.0
|
| 378 |
+
Netherlands,2011.0,4.0,112.0,2.1235427856445312,1.0,1.1905420883452453,5.0,11.077327728271484,6.699999809265137,1.0
|
| 379 |
+
Netherlands,2012.0,4.0,113.0,2.0374481678009033,1.0,-1.422504187396391,5.0,11.077327728271484,6.699999809265137,1.0
|
| 380 |
+
Netherlands,2013.0,4.0,113.0,2.015455722808838,1.0,-0.7883062120658814,5.800000190734863,15.411633491516113,5.699999809265137,1.0
|
| 381 |
+
Netherlands,2014.0,4.0,113.0,1.9150705337524414,1.0,0.6483150537124016,7.300000190734863,15.411633491516113,5.699999809265137,1.0
|
| 382 |
+
Netherlands,2015.0,4.0,113.0,1.941355586051941,1.0,1.5599445091231003,7.400000095367432,15.411633491516113,5.699999809265137,1.0
|
| 383 |
+
Netherlands,2016.0,4.0,113.0,1.8445212841033936,1.0,1.5599445091231003,6.900000095367432,15.411633491516113,5.699999809265137,1.0
|
| 384 |
+
Poland,2004.0,3.0,114.0,2.2968311309814453,0.0,5.1971915250183205,,18.57185935974121,3.5999999046325684,0.0
|
| 385 |
+
Poland,2005.0,3.0,115.0,2.408215045928955,0.0,3.5925756652070673,19.100000381469727,18.57185935974121,3.5999999046325684,0.0
|
| 386 |
+
Poland,2006.0,3.0,115.0,2.486022710800171,0.0,6.260005792106318,17.899999618530273,4.2291669845581055,4.300000190734863,0.0
|
| 387 |
+
Poland,2007.0,3.0,116.0,2.4592978954315186,0.0,7.259915124976833,13.899999618530273,4.2291669845581055,4.300000190734863,0.0
|
| 388 |
+
Poland,2008.0,3.0,116.0,2.429103136062622,0.0,3.90620822624866,9.600000381469727,3.8649885654449463,2.799999952316284,0.0
|
| 389 |
+
Poland,2009.0,3.0,116.0,2.3792126178741455,0.0,2.564656681776065,7.099999904632568,3.8649885654449463,2.799999952316284,0.0
|
| 390 |
+
Poland,2010.0,4.0,116.0,2.4216299057006836,0.0,3.9954643244928745,8.100000381469727,3.8649885654449463,2.799999952316284,0.0
|
| 391 |
+
Poland,2011.0,4.0,117.0,2.4733712673187256,0.0,4.952064163442015,9.699999809265137,3.8649885654449463,2.799999952316284,0.0
|
| 392 |
+
Poland,2012.0,4.0,117.0,2.463608503341675,0.0,1.5619495310338856,9.699999809265137,14.505400657653809,3.0,0.0
|
| 393 |
+
Poland,2013.0,4.0,117.0,2.2863047122955322,0.0,1.325847007356763,10.100000381469727,14.505400657653809,3.0,0.0
|
| 394 |
+
Poland,2014.0,4.0,117.0,2.358215570449829,0.0,3.3598700976461124,10.300000190734863,14.505400657653809,3.0,0.0
|
| 395 |
+
Poland,2015.0,4.0,118.0,2.49674391746521,1.0,3.683108422965984,9.0,,,0.0
|
| 396 |
+
Poland,2016.0,4.0,118.0,2.3014750480651855,1.0,3.683108422965984,7.5,,,0.0
|
| 397 |
+
Portugal,1985.0,1.0,119.0,2.1137964725494385,0.0,2.5266056625325293,,19.795251846313477,3.4000000953674316,0.0
|
| 398 |
+
Portugal,1986.0,1.0,119.0,1.9849910736083984,0.0,4.04627863059346,9.800000190734863,20.82263946533203,4.300000190734863,0.0
|
| 399 |
+
Portugal,1987.0,1.0,120.0,2.1278038024902344,0.0,6.410062675250994,9.5,20.82263946533203,4.300000190734863,0.0
|
| 400 |
+
Portugal,1988.0,1.0,120.0,2.120853900909424,0.0,7.600903243801319,8.0,23.20338249206543,2.4000000953674316,0.0
|
| 401 |
+
Portugal,1989.0,1.0,120.0,1.9991105794906616,0.0,6.5960709725222175,6.699999809265137,23.20338249206543,2.4000000953674316,0.0
|
| 402 |
+
Portugal,1990.0,2.0,120.0,1.7881757020950317,0.0,4.1773290098717455,6.0,23.20338249206543,2.4000000953674316,0.0
|
| 403 |
+
Portugal,1991.0,2.0,121.0,1.9027873277664185,0.0,4.60903352475544,5.599999904632568,23.20338249206543,2.4000000953674316,0.0
|
| 404 |
+
Portugal,1992.0,2.0,121.0,1.9384100437164307,0.0,1.1681033517870925,5.0,5.207034111022949,2.200000047683716,0.0
|
| 405 |
+
Portugal,1993.0,2.0,121.0,1.7985217571258545,0.0,-2.1630211266946593,5.0,5.207034111022949,2.200000047683716,0.0
|
| 406 |
+
Portugal,1994.0,2.0,121.0,1.9798837900161743,0.0,0.6935177084026307,6.300000190734863,5.207034111022949,2.200000047683716,0.0
|
| 407 |
+
Portugal,1995.0,2.0,122.0,2.1213345527648926,0.0,3.9223735308329926,7.599999904632568,5.207034111022949,2.200000047683716,0.0
|
| 408 |
+
Portugal,1996.0,2.0,122.0,2.092378616333008,0.0,3.108270028962741,7.900000095367432,11.054967880249023,2.5999999046325684,0.0
|
| 409 |
+
Portugal,1997.0,2.0,122.0,1.9814629554748535,0.0,3.9610290953515888,8.0,11.054967880249023,2.5999999046325684,0.0
|
| 410 |
+
Portugal,1998.0,2.0,122.0,1.9918627738952637,0.0,4.2635004714393565,7.5,11.054967880249023,2.5999999046325684,0.0
|
| 411 |
+
Portugal,1999.0,2.0,123.0,1.9786174297332764,0.0,3.30224874904702,6.099999904632568,11.054967880249023,2.5999999046325684,0.0
|
| 412 |
+
Portugal,2000.0,3.0,123.0,2.006732225418091,0.0,3.060570908033983,5.5,11.704682350158691,2.5999999046325684,0.0
|
| 413 |
+
Portugal,2001.0,3.0,123.0,1.9249943494796753,0.0,1.2268983468484334,5.099999904632568,11.704682350158691,2.5999999046325684,0.0
|
| 414 |
+
Portugal,2002.0,3.0,124.0,2.0328824520111084,0.0,0.2184269255451142,5.099999904632568,11.704682350158691,2.5999999046325684,0.0
|
| 415 |
+
Portugal,2003.0,3.0,124.0,1.9966661930084229,0.0,-1.305413412199608,6.199999809265137,15.094148635864258,2.5999999046325684,0.0
|
| 416 |
+
Portugal,2004.0,3.0,124.0,1.9709694385528564,0.0,1.5684135002011457,7.400000095367432,15.094148635864258,2.5999999046325684,0.0
|
| 417 |
+
Portugal,2005.0,3.0,125.0,2.0347065925598145,0.0,0.5800034405230208,7.800000190734863,15.094148635864258,2.5999999046325684,0.0
|
| 418 |
+
Portugal,2006.0,3.0,125.0,2.0909855365753174,0.0,1.3700846178873165,8.800000190734863,15.412256240844727,2.5999999046325684,0.0
|
| 419 |
+
Portugal,2007.0,3.0,125.0,2.1480462551116943,0.0,2.2910035293566247,8.899999618530273,15.412256240844727,2.5999999046325684,0.0
|
| 420 |
+
Portugal,2008.0,3.0,125.0,1.9677385091781616,0.0,0.05489775061316175,9.100000381469727,15.412256240844727,2.5999999046325684,0.0
|
| 421 |
+
Portugal,2009.0,3.0,126.0,2.0581235885620117,0.0,-3.0705520177398355,8.800000190734863,15.412256240844727,2.5999999046325684,0.0
|
| 422 |
+
Portugal,2010.0,4.0,126.0,1.9379907846450806,0.0,1.851920762998532,10.699999809265137,16.568023681640625,3.0999999046325684,0.0
|
| 423 |
+
Portugal,2011.0,4.0,127.0,2.1298842430114746,0.0,-1.6823488064140124,12.0,16.568023681640625,3.0999999046325684,0.0
|
| 424 |
+
Portugal,2012.0,4.0,127.0,2.010873556137085,0.0,-3.638376561570104,12.899999618530273,21.22355842590332,2.9000000953674316,0.0
|
| 425 |
+
Portugal,2013.0,4.0,127.0,2.3007407188415527,0.0,-0.5860513867485102,15.800000190734863,21.22355842590332,2.9000000953674316,0.0
|
| 426 |
+
Portugal,2014.0,4.0,127.0,1.9096777439117432,0.0,1.4513380861570329,16.399999618530273,21.22355842590332,2.9000000953674316,0.0
|
| 427 |
+
Portugal,2015.0,4.0,128.0,2.0288145542144775,0.0,1.968312016004814,14.100000381469727,,,0.0
|
| 428 |
+
Portugal,2016.0,4.0,128.0,2.106044054031372,0.0,1.968312016004814,12.600000381469727,,,0.0
|
| 429 |
+
Romania,2004.0,3.0,129.0,2.342529058456421,0.0,8.977765964437102,,3.9149699211120605,3.200000047683716,1.0
|
| 430 |
+
Romania,2005.0,3.0,129.0,2.1343631744384766,0.0,4.817198495649771,8.0,16.381454467773438,3.0,1.0
|
| 431 |
+
Romania,2006.0,3.0,129.0,2.4511098861694336,0.0,8.697583854919147,7.099999904632568,16.381454467773438,3.0,1.0
|
| 432 |
+
Romania,2007.0,3.0,129.0,2.3475632667541504,0.0,8.454090831087555,7.199999809265137,16.381454467773438,3.0,1.0
|
| 433 |
+
Romania,2008.0,3.0,130.0,2.368638277053833,0.0,10.281468313361753,6.400000095367432,16.381454467773438,3.0,1.0
|
| 434 |
+
Romania,2009.0,3.0,130.0,2.7342727184295654,0.0,-6.289378338931102,5.599999904632568,10.999716758728027,3.200000047683716,1.0
|
| 435 |
+
Romania,2010.0,4.0,130.0,2.618300437927246,0.0,-0.2074955889739524,6.5,10.999716758728027,3.200000047683716,1.0
|
| 436 |
+
Romania,2011.0,4.0,130.0,2.5856151580810547,0.0,1.5545806747131796,7.0,10.999716758728027,3.200000047683716,1.0
|
| 437 |
+
Romania,2012.0,4.0,131.0,2.6949803829193115,0.0,1.0899958121540225,7.199999809265137,10.999716758728027,3.200000047683716,1.0
|
| 438 |
+
Romania,2013.0,4.0,131.0,2.635117530822754,0.0,3.9167545545724356,6.800000190734863,18.43178939819336,1.899999976158142,1.0
|
| 439 |
+
Romania,2014.0,4.0,131.0,2.7932841777801514,0.0,3.3454295651350616,7.099999904632568,18.43178939819336,1.899999976158142,1.0
|
| 440 |
+
Romania,2015.0,4.0,131.0,2.6899971961975098,0.0,4.137300599240226,6.800000190734863,18.43178939819336,1.899999976158142,1.0
|
| 441 |
+
Romania,2016.0,4.0,132.0,2.723210334777832,0.0,4.137300599240226,6.800000190734863,,,1.0
|
| 442 |
+
Slovenia,2004.0,3.0,133.0,2.335625171661377,0.0,4.284952626107511,,10.040736198425293,4.599999904632568,0.0
|
| 443 |
+
Slovenia,2005.0,3.0,133.0,2.3600640296936035,0.0,3.8230015820264023,6.300000190734863,11.568573951721191,4.699999809265137,0.0
|
| 444 |
+
Slovenia,2006.0,3.0,133.0,2.2227492332458496,0.0,5.3193904947864565,6.5,11.568573951721191,4.699999809265137,0.0
|
| 445 |
+
Slovenia,2007.0,3.0,133.0,2.3696742057800293,0.0,6.345215709668124,6.0,11.568573951721191,4.699999809265137,0.0
|
| 446 |
+
Slovenia,2008.0,3.0,134.0,2.5326106548309326,0.0,3.137058804885463,4.900000095367432,11.568573951721191,4.699999809265137,0.0
|
| 447 |
+
Slovenia,2009.0,3.0,134.0,2.6773998737335205,0.0,-8.62700278288705,4.400000095367432,9.165651321411133,4.199999809265137,0.0
|
| 448 |
+
Slovenia,2010.0,4.0,134.0,2.3999757766723633,0.0,0.7971552502226587,5.900000095367432,9.165651321411133,4.199999809265137,0.0
|
| 449 |
+
Slovenia,2011.0,4.0,135.0,2.460275173187256,0.0,0.44047174870633665,7.300000190734863,9.165651321411133,4.199999809265137,0.0
|
| 450 |
+
Slovenia,2012.0,4.0,135.0,2.6409668922424316,0.0,-2.9222698973401418,8.199999809265137,6.773007869720459,4.5,0.0
|
| 451 |
+
Slovenia,2013.0,4.0,135.0,2.322906970977783,0.0,-1.1920650102837045,8.899999618530273,6.773007869720459,4.5,0.0
|
| 452 |
+
Slovenia,2014.0,4.0,136.0,2.539829969406128,0.0,2.9470618466345306,10.100000381469727,,,0.0
|
| 453 |
+
Slovenia,2015.0,4.0,136.0,2.5734713077545166,0.0,2.790028816149533,9.699999809265137,,,0.0
|
| 454 |
+
Slovenia,2016.0,4.0,136.0,2.3866872787475586,0.0,2.790028816149533,9.0,,,0.0
|
| 455 |
+
Spain,1985.0,1.0,137.0,2.3018062114715576,0.0,1.9479271470242454,,15.085322380065918,2.5,0.0
|
| 456 |
+
Spain,1986.0,1.0,138.0,2.1192989349365234,0.0,2.9403648001359404,17.799999237060547,15.085322380065918,2.5,0.0
|
| 457 |
+
Spain,1987.0,1.0,138.0,1.9916760921478271,0.0,5.287008993978256,17.399999618530273,13.18350601196289,2.9000000953674316,0.0
|
| 458 |
+
Spain,1988.0,1.0,138.0,2.1122443675994873,0.0,4.863512674323486,19.700000762939453,13.18350601196289,2.9000000953674316,0.0
|
| 459 |
+
Spain,1989.0,1.0,139.0,2.235999584197998,0.0,4.622669941710855,18.700000762939453,13.18350601196289,2.9000000953674316,0.0
|
| 460 |
+
Spain,1990.0,2.0,139.0,1.9228259325027466,0.0,3.6238879127036934,16.5,11.02681827545166,2.700000047683716,0.0
|
| 461 |
+
Spain,1991.0,2.0,139.0,1.9521629810333252,0.0,2.3126355761268687,15.5,11.02681827545166,2.700000047683716,0.0
|
| 462 |
+
Spain,1992.0,2.0,139.0,2.0712125301361084,0.0,0.5967368956967221,15.5,11.02681827545166,2.700000047683716,0.0
|
| 463 |
+
Spain,1993.0,2.0,140.0,2.237067699432373,0.0,-1.338718060998329,17.0,11.02681827545166,2.700000047683716,0.0
|
| 464 |
+
Spain,1994.0,2.0,140.0,2.2559351921081543,0.0,2.108140077710524,20.799999237060547,10.203544616699219,2.5999999046325684,0.0
|
| 465 |
+
Spain,1995.0,2.0,140.0,2.0838372707366943,0.0,2.517343134243211,22.0,10.203544616699219,2.5999999046325684,0.0
|
| 466 |
+
Spain,1996.0,2.0,141.0,2.0918750762939453,0.0,2.43752725650758,20.700000762939453,10.203544616699219,2.5999999046325684,0.0
|
| 467 |
+
Spain,1997.0,2.0,141.0,2.0595531463623047,0.0,3.416582200241432,19.899999618530273,10.382596015930176,2.700000047683716,0.0
|
| 468 |
+
Spain,1998.0,2.0,141.0,1.9613237380981445,0.0,3.941771142363144,18.399999618530273,10.382596015930176,2.700000047683716,0.0
|
| 469 |
+
Spain,1999.0,2.0,141.0,1.929906964302063,0.0,3.948008470859381,16.399999618530273,10.382596015930176,2.700000047683716,0.0
|
| 470 |
+
Spain,2000.0,3.0,142.0,1.9710729122161865,0.0,4.407967603259948,13.600000381469727,10.382596015930176,2.700000047683716,0.0
|
| 471 |
+
Spain,2001.0,3.0,142.0,1.8516496419906616,0.0,2.7435816005254012,11.899999618530273,8.502665519714355,2.5,0.0
|
| 472 |
+
Spain,2002.0,3.0,142.0,1.839176893234253,0.0,1.2022692433046493,10.600000381469727,8.502665519714355,2.5,0.0
|
| 473 |
+
Spain,2003.0,3.0,142.0,1.8846403360366821,0.0,1.3382437553063906,11.5,8.502665519714355,2.5,0.0
|
| 474 |
+
Spain,2004.0,3.0,143.0,2.03003191947937,0.0,1.4019086802391079,11.5,8.502665519714355,2.5,0.0
|
| 475 |
+
Spain,2005.0,3.0,143.0,1.9158600568771362,0.0,1.9855754664744933,11.0,7.692877769470215,2.5,0.0
|
| 476 |
+
Spain,2006.0,3.0,143.0,1.80743408203125,0.0,2.428008518804211,9.199999809265137,7.692877769470215,2.5,0.0
|
| 477 |
+
Spain,2007.0,3.0,143.0,1.9034863710403442,0.0,1.8657176646781504,8.5,7.692877769470215,2.5,0.0
|
| 478 |
+
Spain,2008.0,3.0,144.0,2.0963656902313232,0.0,-0.4843619429275993,8.199999809265137,7.692877769470215,2.5,0.0
|
| 479 |
+
Spain,2009.0,3.0,144.0,2.112072229385376,0.0,-4.42412216153516,11.300000190734863,6.908108711242676,2.299999952316284,0.0
|
| 480 |
+
Spain,2010.0,4.0,144.0,2.091264247894287,0.0,-0.44562683563616307,17.899999618530273,6.908108711242676,2.299999952316284,0.0
|
| 481 |
+
Spain,2011.0,4.0,145.0,2.0327391624450684,0.0,-1.351240940561118,19.899999618530273,6.908108711242676,2.299999952316284,0.0
|
| 482 |
+
Spain,2012.0,4.0,145.0,2.1960790157318115,0.0,-2.6835126942141865,21.399999618530273,8.875782012939453,2.5999999046325684,0.0
|
| 483 |
+
Spain,2013.0,4.0,145.0,2.0399045944213867,0.0,-1.3492529063845773,24.799999237060547,8.875782012939453,2.5999999046325684,0.0
|
| 484 |
+
Spain,2014.0,4.0,145.0,2.047776460647583,0.0,1.6641452130041667,26.100000381469727,8.875782012939453,2.5999999046325684,0.0
|
| 485 |
+
Spain,2015.0,4.0,146.0,1.885252833366394,0.0,3.35351081203078,24.5,8.875782012939453,2.5999999046325684,0.0
|
| 486 |
+
Spain,2016.0,4.0,147.0,2.112290382385254,0.0,3.35351081203078,22.100000381469727,15.3900785446167,4.5,0.0
|
| 487 |
+
Sweden,1994.0,2.0,148.0,,0.0,3.350468242264174,,28.60192108154297,4.199999809265137,1.0
|
| 488 |
+
Sweden,1995.0,2.0,148.0,2.0298495292663574,0.0,3.4799163715083163,9.399999618530273,22.22006607055664,3.5,1.0
|
| 489 |
+
Sweden,1996.0,2.0,148.0,2.0779709815979004,0.0,1.3564270196943922,8.800000190734863,22.22006607055664,3.5,1.0
|
| 490 |
+
Sweden,1997.0,2.0,148.0,2.063025712966919,0.0,2.8415766424676403,9.600000381469727,22.22006607055664,3.5,1.0
|
| 491 |
+
Sweden,1998.0,2.0,149.0,2.092111587524414,0.0,4.168949245471714,9.899999618530273,22.22006607055664,3.5,1.0
|
| 492 |
+
Sweden,1999.0,2.0,149.0,2.0544071197509766,0.0,4.448727112499457,8.199999809265137,26.964141845703125,4.300000190734863,1.0
|
| 493 |
+
Sweden,2000.0,3.0,149.0,2.033555507659912,0.0,4.567242893439471,6.699999809265137,26.964141845703125,4.300000190734863,1.0
|
| 494 |
+
Sweden,2001.0,3.0,149.0,2.021242380142212,0.0,1.2911081444663672,5.599999904632568,26.964141845703125,4.300000190734863,1.0
|
| 495 |
+
Sweden,2002.0,3.0,150.0,2.0843870639801025,0.0,1.7419308797618538,5.800000190734863,26.964141845703125,4.300000190734863,1.0
|
| 496 |
+
Sweden,2003.0,3.0,150.0,2.1019630432128906,0.0,2.005478024910221,6.0,24.772695541381836,4.199999809265137,1.0
|
| 497 |
+
Sweden,2004.0,3.0,150.0,2.0577213764190674,0.0,3.9110439981597036,6.599999904632568,24.772695541381836,4.199999809265137,1.0
|
| 498 |
+
Sweden,2005.0,3.0,150.0,2.0319652557373047,0.0,2.4079363657002477,7.400000095367432,24.772695541381836,4.199999809265137,1.0
|
| 499 |
+
Sweden,2006.0,3.0,151.0,2.137139081954956,0.0,4.1009262886298155,7.699999809265137,24.772695541381836,4.199999809265137,1.0
|
| 500 |
+
Sweden,2007.0,3.0,151.0,2.1282949447631836,0.0,2.640983001474637,7.099999904632568,17.317785263061523,4.099999904632568,1.0
|
| 501 |
+
Sweden,2008.0,3.0,151.0,2.2192330360412598,0.0,-1.3287315917732199,6.099999904632568,17.317785263061523,4.099999904632568,1.0
|
| 502 |
+
Sweden,2009.0,3.0,151.0,2.256427764892578,0.0,-5.9889642836632655,6.199999809265137,17.317785263061523,4.099999904632568,1.0
|
| 503 |
+
Sweden,2010.0,4.0,152.0,2.2797911167144775,1.0,5.08918555076796,8.300000190734863,17.317785263061523,4.099999904632568,1.0
|
| 504 |
+
Sweden,2011.0,4.0,152.0,2.233771562576294,1.0,1.892057410180329,8.600000381469727,14.506769180297852,4.5,1.0
|
| 505 |
+
Sweden,2012.0,4.0,152.0,2.239905834197998,1.0,-1.021244094582471,7.800000190734863,14.506769180297852,4.5,1.0
|
| 506 |
+
Sweden,2013.0,4.0,152.0,2.131387710571289,1.0,0.3869632457747276,8.0,14.506769180297852,4.5,1.0
|
| 507 |
+
Sweden,2014.0,4.0,152.0,2.2904305458068848,1.0,1.257282769689436,8.0,14.506769180297852,4.5,1.0
|
| 508 |
+
Sweden,2015.0,4.0,153.0,2.2931125164031982,1.0,3.0020047944359716,7.900000095367432,20.853548049926758,5.0,1.0
|
| 509 |
+
Sweden,2016.0,4.0,153.0,2.234025239944458,1.0,3.0020047944359716,7.400000095367432,20.853548049926758,5.0,1.0
|
| 510 |
+
United Kingdom,1973.0,0.0,154.0,2.4635355472564697,0.0,6.334341766903404,,10.201143264770508,2.0,0.0
|
| 511 |
+
United Kingdom,1974.0,0.0,155.0,,0.0,6.334341766903404,2.200000047683716,10.201143264770508,2.0,0.0
|
| 512 |
+
United Kingdom,1975.0,0.0,155.0,,0.0,6.334341766903404,2.0,22.32686996459961,2.0999999046325684,0.0
|
| 513 |
+
United Kingdom,1976.0,0.0,155.0,2.051785945892334,0.0,3.0536414703565673,3.200000047683716,22.32686996459961,2.0999999046325684,0.0
|
| 514 |
+
United Kingdom,1977.0,0.0,155.0,1.9630358219146729,0.0,2.6307732965243003,4.800000190734863,22.32686996459961,2.0999999046325684,0.0
|
| 515 |
+
United Kingdom,1978.0,0.0,155.0,1.9967458248138428,0.0,4.124361350864618,5.099999904632568,22.32686996459961,2.0999999046325684,0.0
|
| 516 |
+
United Kingdom,1979.0,0.0,156.0,1.948274850845337,0.0,3.5831836263387706,5.0,22.32686996459961,2.0999999046325684,0.0
|
| 517 |
+
United Kingdom,1980.0,1.0,156.0,1.8584057092666626,0.0,-2.2861551505548583,4.599999904632568,27.492969512939453,2.0,0.0
|
| 518 |
+
United Kingdom,1981.0,1.0,156.0,1.9951623678207397,0.0,-0.8813581373644472,5.599999904632568,27.492969512939453,2.0,0.0
|
| 519 |
+
United Kingdom,1982.0,1.0,156.0,1.9745832681655884,0.0,2.111988009525638,8.800000190734863,27.492969512939453,2.0,0.0
|
| 520 |
+
United Kingdom,1983.0,1.0,157.0,1.9624080657958984,0.0,4.166259923200371,10.100000381469727,27.492969512939453,2.0,0.0
|
| 521 |
+
United Kingdom,1984.0,1.0,157.0,1.9575761556625366,0.0,2.098675540154545,10.800000190734863,27.95818328857422,2.0,0.0
|
| 522 |
+
United Kingdom,1985.0,1.0,157.0,1.9663326740264893,0.0,3.8772302327585857,10.899999618530273,27.95818328857422,2.0,0.0
|
| 523 |
+
United Kingdom,1986.0,1.0,157.0,1.7873115539550781,0.0,2.9296720624940975,11.199999809265137,27.95818328857422,2.0,0.0
|
| 524 |
+
United Kingdom,1987.0,1.0,158.0,1.8813459873199463,0.0,5.335866854476464,11.199999809265137,27.95818328857422,2.0,0.0
|
| 525 |
+
United Kingdom,1988.0,1.0,158.0,2.0976266860961914,0.0,5.685099629758158,10.300000190734863,19.391698837280273,2.0,0.0
|
| 526 |
+
United Kingdom,1989.0,1.0,158.0,2.068805456161499,0.0,2.2525756472345604,8.5,19.391698837280273,2.0,0.0
|
| 527 |
+
United Kingdom,1990.0,2.0,158.0,1.8451786041259766,0.0,0.2534453482865047,7.099999904632568,19.391698837280273,2.0,0.0
|
| 528 |
+
United Kingdom,1991.0,2.0,158.0,1.9475276470184326,0.0,-1.5619343701246184,6.900000095367432,19.391698837280273,2.0,0.0
|
| 529 |
+
United Kingdom,1992.0,2.0,159.0,2.019979476928711,0.0,0.1751366845584301,8.600000381469727,19.391698837280273,2.0,0.0
|
| 530 |
+
United Kingdom,1993.0,2.0,159.0,1.9015191793441772,0.0,2.389231885918027,9.800000190734863,20.79439926147461,2.200000047683716,0.0
|
| 531 |
+
United Kingdom,1994.0,2.0,159.0,1.8554065227508545,0.0,3.7594739591240813,10.199999809265137,20.79439926147461,2.200000047683716,0.0
|
| 532 |
+
United Kingdom,1995.0,2.0,159.0,1.7938096523284912,0.0,2.2508660297939067,9.300000190734863,20.79439926147461,2.200000047683716,0.0
|
| 533 |
+
United Kingdom,1996.0,2.0,159.0,1.9417682886123657,0.0,2.4067258454415867,8.5,20.79439926147461,2.200000047683716,0.0
|
| 534 |
+
United Kingdom,1997.0,2.0,160.0,1.8813667297363281,0.0,2.8337674151698664,7.900000095367432,20.79439926147461,2.200000047683716,0.0
|
| 535 |
+
United Kingdom,1998.0,2.0,160.0,1.831242561340332,0.0,3.0756613458106954,6.800000190734863,11.250272750854492,2.0999999046325684,0.0
|
| 536 |
+
United Kingdom,1999.0,2.0,160.0,1.8410545587539673,0.0,2.7695325397665447,6.099999904632568,11.250272750854492,2.0999999046325684,0.0
|
| 537 |
+
United Kingdom,2000.0,3.0,160.0,1.8089269399642944,0.0,3.428632217024652,5.900000095367432,11.250272750854492,2.0999999046325684,0.0
|
| 538 |
+
United Kingdom,2001.0,3.0,161.0,1.7505075931549072,0.0,2.363202221260742,5.400000095367432,11.250272750854492,2.0999999046325684,0.0
|
| 539 |
+
United Kingdom,2002.0,3.0,161.0,1.8186335563659668,0.0,2.0609366467633654,5.0,14.773595809936523,2.0999999046325684,0.0
|
| 540 |
+
United Kingdom,2003.0,3.0,161.0,1.8093690872192383,0.0,2.85660512013168,5.099999904632568,14.773595809936523,2.0999999046325684,0.0
|
| 541 |
+
United Kingdom,2004.0,3.0,161.0,1.79267156124115,0.0,1.9070127115971978,5.0,14.773595809936523,2.0999999046325684,0.0
|
| 542 |
+
United Kingdom,2005.0,3.0,162.0,1.7930405139923096,0.0,2.2915958814914577,4.699999809265137,14.773595809936523,2.0999999046325684,0.0
|
| 543 |
+
United Kingdom,2006.0,3.0,162.0,1.822739839553833,0.0,1.9099747917157601,4.800000190734863,8.930902481079102,2.200000047683716,0.0
|
| 544 |
+
United Kingdom,2007.0,3.0,162.0,1.8236674070358276,0.0,1.7903358260372935,5.400000095367432,8.930902481079102,2.200000047683716,0.0
|
| 545 |
+
United Kingdom,2008.0,3.0,162.0,1.949040174484253,0.0,-1.2471603450262296,5.300000190734863,8.930902481079102,2.200000047683716,0.0
|
| 546 |
+
United Kingdom,2009.0,3.0,162.0,1.776461124420166,0.0,-4.913894409559788,5.599999904632568,8.930902481079102,2.200000047683716,0.0
|
| 547 |
+
United Kingdom,2010.0,4.0,163.0,1.8258851766586304,0.0,0.747326325510461,7.599999904632568,8.930902481079102,2.200000047683716,0.0
|
| 548 |
+
United Kingdom,2011.0,4.0,163.0,1.8410437107086182,0.0,1.1784107362645715,7.800000190734863,9.71563720703125,2.4000000953674316,0.0
|
| 549 |
+
United Kingdom,2012.0,4.0,163.0,1.8381925821304321,0.0,0.4779822796101611,8.100000381469727,9.71563720703125,2.4000000953674316,0.0
|
| 550 |
+
United Kingdom,2013.0,4.0,163.0,1.7305223941802979,0.0,1.478193464435193,7.900000095367432,9.71563720703125,2.4000000953674316,0.0
|
| 551 |
+
United Kingdom,2014.0,4.0,163.0,1.8038872480392456,0.0,2.080913253365136,7.599999904632568,9.71563720703125,2.4000000953674316,0.0
|
| 552 |
+
United Kingdom,2015.0,4.0,164.0,1.998780369758606,0.0,1.5043192235417802,6.099999904632568,9.71563720703125,2.4000000953674316,0.0
|
| 553 |
+
United Kingdom,2016.0,4.0,164.0,1.8086107969284058,0.0,1.5043192235417802,4.800000190734863,9.71563720703125,2.5,0.0
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/data/metadata.txt
ADDED
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| 1 |
+
| Variable Name | Description |
|
| 2 |
+
|---------------|-------------------------------------------------------------------------------|
|
| 3 |
+
| country | Country name (EU member state covered in the panel). |
|
| 4 |
+
| year | Calendar year of observation. |
|
| 5 |
+
| decade | Decade identifier for the year (broader time block used in modeling). |
|
| 6 |
+
| cltreat | - |
|
| 7 |
+
| polarization | Standard deviation of respondents’ left–right self-placement (country-year). |
|
| 8 |
+
| rtreatment | Indicator for years at or after the first national-parliament entry of a radical-right party in that country. |
|
| 9 |
+
| gdpgrowth | Annual GDP growth (percent), from standard national/economic statistics. |
|
| 10 |
+
| lunemployment | Unemployment rate lagged one year (percent). |
|
| 11 |
+
| lparpol | Party-system polarization index lagged one year. |
|
| 12 |
+
| lenp | Effective number of parliamentary parties (ENPP) lagged one year. |
|
| 13 |
+
| threshold | Indicator for the presence of a formal national electoral threshold that year.|
|
| 14 |
+
|
| 15 |
+
Data Description: This file is a country–year panel spanning 17 European countries from 1973 to 2016. It was built by aggregating Eurobarometer survey waves to produce an annual measure of ideological dispersion among citizens (the standard deviation of left–right self-placements) and merging these aggregates with information on when radical-right parties first held seats in national parliaments, institutional features of electoral systems, and macro- and party-system characteristics. The macroeconomic series (GDP growth, unemployment) come from standard official or widely used statistical sources, while party-system indicators (effective number of parliamentary parties and party-system polarization) are derived from election-based and expert data commonly used in comparative politics. The study provides context on the emergence and parliamentary recognition of radical-right parties across Europe and how this period coincided with shifts in citizens’ left–right placements observed in repeated Eurobarometer surveys.
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/all_q.py
ADDED
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|
|
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|
|
|
|
|
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|
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|
|
|
|
|
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|
|
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|
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|
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|
|
|
|
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|
|
|
|
|
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|
|
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|
|
|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import sys
|
| 2 |
+
import json
|
| 3 |
+
import os
|
| 4 |
+
import numpy as np
|
| 5 |
+
import pandas as pd
|
| 6 |
+
import statsmodels.api as sm
|
| 7 |
+
|
| 8 |
+
def load_data(path):
|
| 9 |
+
df = pd.read_csv(path)
|
| 10 |
+
# Standardize columns and types
|
| 11 |
+
required_cols = ["country", "year", "polarization", "rtreatment"]
|
| 12 |
+
for col in required_cols:
|
| 13 |
+
if col not in df.columns:
|
| 14 |
+
raise ValueError(f"Missing required column: {col}")
|
| 15 |
+
# Coerce types
|
| 16 |
+
df["country"] = df["country"].astype(str)
|
| 17 |
+
df["year"] = pd.to_numeric(df["year"], errors="coerce").astype("Int64")
|
| 18 |
+
df["rtreatment"] = pd.to_numeric(df["rtreatment"], errors="coerce")
|
| 19 |
+
df["polarization"] = pd.to_numeric(df["polarization"], errors="coerce")
|
| 20 |
+
# Drop rows with missing key fields
|
| 21 |
+
df = df.dropna(subset=["country", "year", "rtreatment", "polarization"])
|
| 22 |
+
# Keep only 0/1 for treatment
|
| 23 |
+
df["rtreatment"] = (df["rtreatment"] > 0).astype(int)
|
| 24 |
+
# Deduplicate if any
|
| 25 |
+
df = df.sort_values(["country", "year"]).drop_duplicates(["country", "year"])
|
| 26 |
+
return df
|
| 27 |
+
|
| 28 |
+
def identify_events(df):
|
| 29 |
+
events = []
|
| 30 |
+
for country, g in df.groupby("country"):
|
| 31 |
+
g = g.sort_values("year")
|
| 32 |
+
treated_years = g.loc[g["rtreatment"] == 1, "year"]
|
| 33 |
+
if treated_years.empty:
|
| 34 |
+
continue # never treated
|
| 35 |
+
t0 = int(treated_years.min())
|
| 36 |
+
# Ensure there is at least one pre-treatment period with 0 treatment
|
| 37 |
+
has_pre_zero = ((g["year"] < t0) & (g["rtreatment"] == 0)).any()
|
| 38 |
+
if not has_pre_zero:
|
| 39 |
+
continue # always treated or treatment already on when panel starts
|
| 40 |
+
# Pick pre year = latest year < t0 with rtreatment == 0 and non-missing outcome
|
| 41 |
+
pre_candidates = g[(g["year"] < t0) & (g["rtreatment"] == 0) & g["polarization"].notna()]
|
| 42 |
+
if pre_candidates.empty:
|
| 43 |
+
continue
|
| 44 |
+
pre_year = int(pre_candidates["year"].max())
|
| 45 |
+
# Pick post year = earliest year >= t0 with rtreatment == 1 and non-missing outcome
|
| 46 |
+
post_candidates = g[(g["year"] >= t0) & (g["rtreatment"] == 1) & g["polarization"].notna()]
|
| 47 |
+
if post_candidates.empty:
|
| 48 |
+
continue
|
| 49 |
+
post_year = int(post_candidates["year"].min())
|
| 50 |
+
|
| 51 |
+
# Determine available controls: countries with rtreatment==0 in both pre and post years and non-missing outcome
|
| 52 |
+
controls = []
|
| 53 |
+
for c2, g2 in df.groupby("country"):
|
| 54 |
+
if c2 == country:
|
| 55 |
+
continue
|
| 56 |
+
g2_pre = g2[g2["year"] == pre_year]
|
| 57 |
+
g2_post = g2[g2["year"] == post_year]
|
| 58 |
+
if len(g2_pre) == 1 and len(g2_post) == 1:
|
| 59 |
+
if (g2_pre["rtreatment"].iloc[0] == 0) and (g2_post["rtreatment"].iloc[0] == 0):
|
| 60 |
+
if np.isfinite(g2_pre["polarization"].iloc[0]) and np.isfinite(g2_post["polarization"].iloc[0]):
|
| 61 |
+
controls.append(c2)
|
| 62 |
+
|
| 63 |
+
if len(controls) >= 2: # require at least 2 control units for a reasonable 2x2
|
| 64 |
+
events.append({
|
| 65 |
+
"country": country,
|
| 66 |
+
"t0": t0,
|
| 67 |
+
"pre_year": pre_year,
|
| 68 |
+
"post_year": post_year,
|
| 69 |
+
"controls": controls,
|
| 70 |
+
"n_controls": len(controls)
|
| 71 |
+
})
|
| 72 |
+
# Sort by most controls, then earliest post_year
|
| 73 |
+
events.sort(key=lambda x: (-x["n_controls"], x["post_year"]))
|
| 74 |
+
return events
|
| 75 |
+
|
| 76 |
+
def estimate_2x2_did(df, event):
|
| 77 |
+
treated = event["country"]
|
| 78 |
+
pre_year = event["pre_year"]
|
| 79 |
+
post_year = event["post_year"]
|
| 80 |
+
controls = event["controls"]
|
| 81 |
+
|
| 82 |
+
subset = df[
|
| 83 |
+
(df["year"].isin([pre_year, post_year])) &
|
| 84 |
+
(df["country"].isin([treated] + controls))
|
| 85 |
+
].copy()
|
| 86 |
+
|
| 87 |
+
# Build DiD variables
|
| 88 |
+
subset["post"] = (subset["year"] == post_year).astype(int)
|
| 89 |
+
subset["treated_group"] = (subset["country"] == treated).astype(int)
|
| 90 |
+
subset["did"] = subset["post"] * subset["treated_group"]
|
| 91 |
+
|
| 92 |
+
# Design matrix
|
| 93 |
+
X = subset[["treated_group", "post", "did"]]
|
| 94 |
+
X = sm.add_constant(X)
|
| 95 |
+
y = subset["polarization"].values
|
| 96 |
+
|
| 97 |
+
# Cluster by country
|
| 98 |
+
groups = subset["country"].astype("category").cat.codes.values
|
| 99 |
+
|
| 100 |
+
model = sm.OLS(y, X)
|
| 101 |
+
res = model.fit(cov_type='cluster', cov_kwds={'groups': groups})
|
| 102 |
+
|
| 103 |
+
att = res.params.get("did", np.nan)
|
| 104 |
+
se = res.bse.get("did", np.nan)
|
| 105 |
+
pval = float(res.pvalues.get("did", np.nan))
|
| 106 |
+
ci_low = float(att - 1.96 * se) if np.isfinite(att) and np.isfinite(se) else None
|
| 107 |
+
ci_high = float(att + 1.96 * se) if np.isfinite(att) and np.isfinite(se) else None
|
| 108 |
+
|
| 109 |
+
return {
|
| 110 |
+
"att": float(att) if np.isfinite(att) else None,
|
| 111 |
+
"se": float(se) if np.isfinite(se) else None,
|
| 112 |
+
"pval": pval if np.isfinite(pval) else None,
|
| 113 |
+
"ci_low": ci_low,
|
| 114 |
+
"ci_high": ci_high,
|
| 115 |
+
"n_obs": int(subset.shape[0]),
|
| 116 |
+
"n_controls": len(controls),
|
| 117 |
+
"treated": treated,
|
| 118 |
+
"pre_year": pre_year,
|
| 119 |
+
"post_year": post_year,
|
| 120 |
+
"controls_list": controls
|
| 121 |
+
}
|
| 122 |
+
|
| 123 |
+
def make_json_payload(est, idx):
|
| 124 |
+
treated = est["treated"]
|
| 125 |
+
pre_year = est["pre_year"]
|
| 126 |
+
post_year = est["post_year"]
|
| 127 |
+
controls_desc = ", ".join(est["controls_list"][:5]) + ("..." if len(est["controls_list"]) > 5 else "")
|
| 128 |
+
exact_q = (
|
| 129 |
+
f"ATT of radical-right party entry (rtreatment) on polarization using a 2x2 DiD: "
|
| 130 |
+
f"treated cohort = {treated} with pre = {pre_year} and post = {post_year}; "
|
| 131 |
+
f"controls = countries with rtreatment = 0 in both years."
|
| 132 |
+
)
|
| 133 |
+
layman = (
|
| 134 |
+
f"Did polarization change in {treated} after its first radical-right entry compared to similar countries "
|
| 135 |
+
f"that had not yet experienced such entry, from {pre_year} to {post_year}?"
|
| 136 |
+
)
|
| 137 |
+
payload = {
|
| 138 |
+
"identification_strategy": {
|
| 139 |
+
"strategy": "Difference-in-Differences",
|
| 140 |
+
"variant": "sharp 2x2 (single-cohort)",
|
| 141 |
+
"treatments": ["rtreatment"],
|
| 142 |
+
"outcomes": ["polarization"],
|
| 143 |
+
"outcome_is_stacked": False,
|
| 144 |
+
"controls": None,
|
| 145 |
+
"post_treatment_variables": None,
|
| 146 |
+
"minimal_controlling_set": None,
|
| 147 |
+
"reason_for_minimal_controlling_set": None,
|
| 148 |
+
"time_variable": "year",
|
| 149 |
+
"group_variable": "country"
|
| 150 |
+
},
|
| 151 |
+
"quantity": "ATT",
|
| 152 |
+
"estimand_population": f"Treated cohort: {treated}; Controls: not-yet-treated in {pre_year} and {post_year}",
|
| 153 |
+
"quantity_value": est["att"],
|
| 154 |
+
"quantity_ci": None if (est["ci_low"] is None or est["ci_high"] is None) else {
|
| 155 |
+
"lower": est["ci_low"],
|
| 156 |
+
"upper": est["ci_high"],
|
| 157 |
+
"level": 0.95
|
| 158 |
+
},
|
| 159 |
+
"standard_error": est["se"],
|
| 160 |
+
"p_value": est["pval"],
|
| 161 |
+
"effect_units": "polarization (std. dev. of left-right placements)",
|
| 162 |
+
"subgroup": None,
|
| 163 |
+
"exact_causal_question": exact_q,
|
| 164 |
+
"layman_query": layman
|
| 165 |
+
}
|
| 166 |
+
return payload
|
| 167 |
+
|
| 168 |
+
def main():
|
| 169 |
+
if len(sys.argv) < 2:
|
| 170 |
+
print("Usage: python code.py data.csv")
|
| 171 |
+
sys.exit(1)
|
| 172 |
+
data_path = sys.argv[1]
|
| 173 |
+
df = load_data(data_path)
|
| 174 |
+
events = identify_events(df)
|
| 175 |
+
|
| 176 |
+
if len(events) == 0:
|
| 177 |
+
print("No eligible 2x2 DiD events found.")
|
| 178 |
+
sys.exit(0)
|
| 179 |
+
|
| 180 |
+
# Select up to 5 best events
|
| 181 |
+
selected = events[:5]
|
| 182 |
+
|
| 183 |
+
for i, ev in enumerate(selected, start=1):
|
| 184 |
+
est = estimate_2x2_did(df, ev)
|
| 185 |
+
payload = make_json_payload(est, i)
|
| 186 |
+
out_path = f"question_{i}.json"
|
| 187 |
+
with open(out_path, "w", encoding="utf-8") as f:
|
| 188 |
+
json.dump(payload, f, ensure_ascii=False, indent=2)
|
| 189 |
+
|
| 190 |
+
if __name__ == "__main__":
|
| 191 |
+
main()
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/estimation_1.py
ADDED
|
@@ -0,0 +1,105 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
import sys
|
| 3 |
+
import numpy as np
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import statsmodels.api as sm
|
| 6 |
+
|
| 7 |
+
def load_data(path):
|
| 8 |
+
df = pd.read_csv(path)
|
| 9 |
+
required_cols = ["country", "year", "polarization", "rtreatment"]
|
| 10 |
+
for col in required_cols:
|
| 11 |
+
if col not in df.columns:
|
| 12 |
+
raise ValueError(f"Missing required column: {col}")
|
| 13 |
+
df["country"] = df["country"].astype(str)
|
| 14 |
+
df["year"] = pd.to_numeric(df["year"], errors="coerce").astype("Int64")
|
| 15 |
+
df["rtreatment"] = pd.to_numeric(df["rtreatment"], errors="coerce")
|
| 16 |
+
df["polarization"] = pd.to_numeric(df["polarization"], errors="coerce")
|
| 17 |
+
df = df.dropna(subset=["country", "year", "rtreatment", "polarization"])
|
| 18 |
+
df["rtreatment"] = (df["rtreatment"] > 0).astype(int)
|
| 19 |
+
df = df.sort_values(["country", "year"]).drop_duplicates(["country", "year"])
|
| 20 |
+
return df
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
def identify_events(df):
|
| 24 |
+
events = []
|
| 25 |
+
for country, g in df.groupby("country"):
|
| 26 |
+
g = g.sort_values("year")
|
| 27 |
+
treated_years = g.loc[g["rtreatment"] == 1, "year"]
|
| 28 |
+
if treated_years.empty:
|
| 29 |
+
continue
|
| 30 |
+
t0 = int(treated_years.min())
|
| 31 |
+
has_pre_zero = ((g["year"] < t0) & (g["rtreatment"] == 0)).any()
|
| 32 |
+
if not has_pre_zero:
|
| 33 |
+
continue
|
| 34 |
+
pre_candidates = g[(g["year"] < t0) & (g["rtreatment"] == 0) & g["polarization"].notna()]
|
| 35 |
+
if pre_candidates.empty:
|
| 36 |
+
continue
|
| 37 |
+
pre_year = int(pre_candidates["year"].max())
|
| 38 |
+
post_candidates = g[(g["year"] >= t0) & (g["rtreatment"] == 1) & g["polarization"].notna()]
|
| 39 |
+
if post_candidates.empty:
|
| 40 |
+
continue
|
| 41 |
+
post_year = int(post_candidates["year"].min())
|
| 42 |
+
controls = []
|
| 43 |
+
for c2, g2 in df.groupby("country"):
|
| 44 |
+
if c2 == country:
|
| 45 |
+
continue
|
| 46 |
+
g2_pre = g2[g2["year"] == pre_year]
|
| 47 |
+
g2_post = g2[g2["year"] == post_year]
|
| 48 |
+
if len(g2_pre) == 1 and len(g2_post) == 1:
|
| 49 |
+
if (g2_pre["rtreatment"].iloc[0] == 0) and (g2_post["rtreatment"].iloc[0] == 0):
|
| 50 |
+
if np.isfinite(g2_pre["polarization"].iloc[0]) and np.isfinite(g2_post["polarization"].iloc[0]):
|
| 51 |
+
controls.append(c2)
|
| 52 |
+
if len(controls) >= 2:
|
| 53 |
+
events.append({
|
| 54 |
+
"country": country,
|
| 55 |
+
"t0": t0,
|
| 56 |
+
"pre_year": pre_year,
|
| 57 |
+
"post_year": post_year,
|
| 58 |
+
"controls": controls,
|
| 59 |
+
"n_controls": len(controls)
|
| 60 |
+
})
|
| 61 |
+
events.sort(key=lambda x: (-x["n_controls"], x["post_year"]))
|
| 62 |
+
return events
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
def estimate_2x2_did(df, event):
|
| 66 |
+
treated = event["country"]
|
| 67 |
+
pre_year = event["pre_year"]
|
| 68 |
+
post_year = event["post_year"]
|
| 69 |
+
controls = event["controls"]
|
| 70 |
+
subset = df[(df["year"].isin([pre_year, post_year])) & (df["country"].isin([treated] + controls))].copy()
|
| 71 |
+
subset["post"] = (subset["year"] == post_year).astype(int)
|
| 72 |
+
subset["treated_group"] = (subset["country"] == treated).astype(int)
|
| 73 |
+
subset["did"] = subset["post"] * subset["treated_group"]
|
| 74 |
+
X = subset[["treated_group", "post", "did"]]
|
| 75 |
+
X = sm.add_constant(X)
|
| 76 |
+
y = subset["polarization"].values
|
| 77 |
+
groups = subset["country"].astype("category").cat.codes.values
|
| 78 |
+
model = sm.OLS(y, X)
|
| 79 |
+
res = model.fit(cov_type='cluster', cov_kwds={'groups': groups})
|
| 80 |
+
att = res.params.get("did", np.nan)
|
| 81 |
+
se = res.bse.get("did", np.nan)
|
| 82 |
+
return (None if not np.isfinite(att) else float(att), None if not np.isfinite(se) else float(se))
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
def main():
|
| 86 |
+
if len(sys.argv) < 2:
|
| 87 |
+
print("effect: None and std_error: None")
|
| 88 |
+
return
|
| 89 |
+
path = sys.argv[1]
|
| 90 |
+
try:
|
| 91 |
+
df = load_data(path)
|
| 92 |
+
except Exception:
|
| 93 |
+
print("effect: None and std_error: None")
|
| 94 |
+
return
|
| 95 |
+
events = identify_events(df)
|
| 96 |
+
# this script corresponds to estimation_1 (first event)
|
| 97 |
+
idx = 0
|
| 98 |
+
if idx >= len(events):
|
| 99 |
+
print("effect: None and std_error: None")
|
| 100 |
+
return
|
| 101 |
+
att, se = estimate_2x2_did(df, events[idx])
|
| 102 |
+
print(f"effect: {att} and std_error: {se}")
|
| 103 |
+
|
| 104 |
+
if __name__ == '__main__':
|
| 105 |
+
main()
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/estimation_2.py
ADDED
|
@@ -0,0 +1,105 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
import sys
|
| 3 |
+
import numpy as np
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import statsmodels.api as sm
|
| 6 |
+
|
| 7 |
+
def load_data(path):
|
| 8 |
+
df = pd.read_csv(path)
|
| 9 |
+
required_cols = ["country", "year", "polarization", "rtreatment"]
|
| 10 |
+
for col in required_cols:
|
| 11 |
+
if col not in df.columns:
|
| 12 |
+
raise ValueError(f"Missing required column: {col}")
|
| 13 |
+
df["country"] = df["country"].astype(str)
|
| 14 |
+
df["year"] = pd.to_numeric(df["year"], errors="coerce").astype("Int64")
|
| 15 |
+
df["rtreatment"] = pd.to_numeric(df["rtreatment"], errors="coerce")
|
| 16 |
+
df["polarization"] = pd.to_numeric(df["polarization"], errors="coerce")
|
| 17 |
+
df = df.dropna(subset=["country", "year", "rtreatment", "polarization"])
|
| 18 |
+
df["rtreatment"] = (df["rtreatment"] > 0).astype(int)
|
| 19 |
+
df = df.sort_values(["country", "year"]).drop_duplicates(["country", "year"])
|
| 20 |
+
return df
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
def identify_events(df):
|
| 24 |
+
events = []
|
| 25 |
+
for country, g in df.groupby("country"):
|
| 26 |
+
g = g.sort_values("year")
|
| 27 |
+
treated_years = g.loc[g["rtreatment"] == 1, "year"]
|
| 28 |
+
if treated_years.empty:
|
| 29 |
+
continue
|
| 30 |
+
t0 = int(treated_years.min())
|
| 31 |
+
has_pre_zero = ((g["year"] < t0) & (g["rtreatment"] == 0)).any()
|
| 32 |
+
if not has_pre_zero:
|
| 33 |
+
continue
|
| 34 |
+
pre_candidates = g[(g["year"] < t0) & (g["rtreatment"] == 0) & g["polarization"].notna()]
|
| 35 |
+
if pre_candidates.empty:
|
| 36 |
+
continue
|
| 37 |
+
pre_year = int(pre_candidates["year"].max())
|
| 38 |
+
post_candidates = g[(g["year"] >= t0) & (g["rtreatment"] == 1) & g["polarization"].notna()]
|
| 39 |
+
if post_candidates.empty:
|
| 40 |
+
continue
|
| 41 |
+
post_year = int(post_candidates["year"].min())
|
| 42 |
+
controls = []
|
| 43 |
+
for c2, g2 in df.groupby("country"):
|
| 44 |
+
if c2 == country:
|
| 45 |
+
continue
|
| 46 |
+
g2_pre = g2[g2["year"] == pre_year]
|
| 47 |
+
g2_post = g2[g2["year"] == post_year]
|
| 48 |
+
if len(g2_pre) == 1 and len(g2_post) == 1:
|
| 49 |
+
if (g2_pre["rtreatment"].iloc[0] == 0) and (g2_post["rtreatment"].iloc[0] == 0):
|
| 50 |
+
if np.isfinite(g2_pre["polarization"].iloc[0]) and np.isfinite(g2_post["polarization"].iloc[0]):
|
| 51 |
+
controls.append(c2)
|
| 52 |
+
if len(controls) >= 2:
|
| 53 |
+
events.append({
|
| 54 |
+
"country": country,
|
| 55 |
+
"t0": t0,
|
| 56 |
+
"pre_year": pre_year,
|
| 57 |
+
"post_year": post_year,
|
| 58 |
+
"controls": controls,
|
| 59 |
+
"n_controls": len(controls)
|
| 60 |
+
})
|
| 61 |
+
events.sort(key=lambda x: (-x["n_controls"], x["post_year"]))
|
| 62 |
+
return events
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
def estimate_2x2_did(df, event):
|
| 66 |
+
treated = event["country"]
|
| 67 |
+
pre_year = event["pre_year"]
|
| 68 |
+
post_year = event["post_year"]
|
| 69 |
+
controls = event["controls"]
|
| 70 |
+
subset = df[(df["year"].isin([pre_year, post_year])) & (df["country"].isin([treated] + controls))].copy()
|
| 71 |
+
subset["post"] = (subset["year"] == post_year).astype(int)
|
| 72 |
+
subset["treated_group"] = (subset["country"] == treated).astype(int)
|
| 73 |
+
subset["did"] = subset["post"] * subset["treated_group"]
|
| 74 |
+
X = subset[["treated_group", "post", "did"]]
|
| 75 |
+
X = sm.add_constant(X)
|
| 76 |
+
y = subset["polarization"].values
|
| 77 |
+
groups = subset["country"].astype("category").cat.codes.values
|
| 78 |
+
model = sm.OLS(y, X)
|
| 79 |
+
res = model.fit(cov_type='cluster', cov_kwds={'groups': groups})
|
| 80 |
+
att = res.params.get("did", np.nan)
|
| 81 |
+
se = res.bse.get("did", np.nan)
|
| 82 |
+
return (None if not np.isfinite(att) else float(att), None if not np.isfinite(se) else float(se))
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
def main():
|
| 86 |
+
if len(sys.argv) < 2:
|
| 87 |
+
print("effect: None and std_error: None")
|
| 88 |
+
return
|
| 89 |
+
path = sys.argv[1]
|
| 90 |
+
try:
|
| 91 |
+
df = load_data(path)
|
| 92 |
+
except Exception:
|
| 93 |
+
print("effect: None and std_error: None")
|
| 94 |
+
return
|
| 95 |
+
events = identify_events(df)
|
| 96 |
+
# this script corresponds to estimation_2 (second event)
|
| 97 |
+
idx = 1
|
| 98 |
+
if idx >= len(events):
|
| 99 |
+
print("effect: None and std_error: None")
|
| 100 |
+
return
|
| 101 |
+
att, se = estimate_2x2_did(df, events[idx])
|
| 102 |
+
print(f"effect: {att} and std_error: {se}")
|
| 103 |
+
|
| 104 |
+
if __name__ == '__main__':
|
| 105 |
+
main()
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/estimation_3.py
ADDED
|
@@ -0,0 +1,105 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
import sys
|
| 3 |
+
import numpy as np
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import statsmodels.api as sm
|
| 6 |
+
|
| 7 |
+
def load_data(path):
|
| 8 |
+
df = pd.read_csv(path)
|
| 9 |
+
required_cols = ["country", "year", "polarization", "rtreatment"]
|
| 10 |
+
for col in required_cols:
|
| 11 |
+
if col not in df.columns:
|
| 12 |
+
raise ValueError(f"Missing required column: {col}")
|
| 13 |
+
df["country"] = df["country"].astype(str)
|
| 14 |
+
df["year"] = pd.to_numeric(df["year"], errors="coerce").astype("Int64")
|
| 15 |
+
df["rtreatment"] = pd.to_numeric(df["rtreatment"], errors="coerce")
|
| 16 |
+
df["polarization"] = pd.to_numeric(df["polarization"], errors="coerce")
|
| 17 |
+
df = df.dropna(subset=["country", "year", "rtreatment", "polarization"])
|
| 18 |
+
df["rtreatment"] = (df["rtreatment"] > 0).astype(int)
|
| 19 |
+
df = df.sort_values(["country", "year"]).drop_duplicates(["country", "year"])
|
| 20 |
+
return df
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
def identify_events(df):
|
| 24 |
+
events = []
|
| 25 |
+
for country, g in df.groupby("country"):
|
| 26 |
+
g = g.sort_values("year")
|
| 27 |
+
treated_years = g.loc[g["rtreatment"] == 1, "year"]
|
| 28 |
+
if treated_years.empty:
|
| 29 |
+
continue
|
| 30 |
+
t0 = int(treated_years.min())
|
| 31 |
+
has_pre_zero = ((g["year"] < t0) & (g["rtreatment"] == 0)).any()
|
| 32 |
+
if not has_pre_zero:
|
| 33 |
+
continue
|
| 34 |
+
pre_candidates = g[(g["year"] < t0) & (g["rtreatment"] == 0) & g["polarization"].notna()]
|
| 35 |
+
if pre_candidates.empty:
|
| 36 |
+
continue
|
| 37 |
+
pre_year = int(pre_candidates["year"].max())
|
| 38 |
+
post_candidates = g[(g["year"] >= t0) & (g["rtreatment"] == 1) & g["polarization"].notna()]
|
| 39 |
+
if post_candidates.empty:
|
| 40 |
+
continue
|
| 41 |
+
post_year = int(post_candidates["year"].min())
|
| 42 |
+
controls = []
|
| 43 |
+
for c2, g2 in df.groupby("country"):
|
| 44 |
+
if c2 == country:
|
| 45 |
+
continue
|
| 46 |
+
g2_pre = g2[g2["year"] == pre_year]
|
| 47 |
+
g2_post = g2[g2["year"] == post_year]
|
| 48 |
+
if len(g2_pre) == 1 and len(g2_post) == 1:
|
| 49 |
+
if (g2_pre["rtreatment"].iloc[0] == 0) and (g2_post["rtreatment"].iloc[0] == 0):
|
| 50 |
+
if np.isfinite(g2_pre["polarization"].iloc[0]) and np.isfinite(g2_post["polarization"].iloc[0]):
|
| 51 |
+
controls.append(c2)
|
| 52 |
+
if len(controls) >= 2:
|
| 53 |
+
events.append({
|
| 54 |
+
"country": country,
|
| 55 |
+
"t0": t0,
|
| 56 |
+
"pre_year": pre_year,
|
| 57 |
+
"post_year": post_year,
|
| 58 |
+
"controls": controls,
|
| 59 |
+
"n_controls": len(controls)
|
| 60 |
+
})
|
| 61 |
+
events.sort(key=lambda x: (-x["n_controls"], x["post_year"]))
|
| 62 |
+
return events
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
def estimate_2x2_did(df, event):
|
| 66 |
+
treated = event["country"]
|
| 67 |
+
pre_year = event["pre_year"]
|
| 68 |
+
post_year = event["post_year"]
|
| 69 |
+
controls = event["controls"]
|
| 70 |
+
subset = df[(df["year"].isin([pre_year, post_year])) & (df["country"].isin([treated] + controls))].copy()
|
| 71 |
+
subset["post"] = (subset["year"] == post_year).astype(int)
|
| 72 |
+
subset["treated_group"] = (subset["country"] == treated).astype(int)
|
| 73 |
+
subset["did"] = subset["post"] * subset["treated_group"]
|
| 74 |
+
X = subset[["treated_group", "post", "did"]]
|
| 75 |
+
X = sm.add_constant(X)
|
| 76 |
+
y = subset["polarization"].values
|
| 77 |
+
groups = subset["country"].astype("category").cat.codes.values
|
| 78 |
+
model = sm.OLS(y, X)
|
| 79 |
+
res = model.fit(cov_type='cluster', cov_kwds={'groups': groups})
|
| 80 |
+
att = res.params.get("did", np.nan)
|
| 81 |
+
se = res.bse.get("did", np.nan)
|
| 82 |
+
return (None if not np.isfinite(att) else float(att), None if not np.isfinite(se) else float(se))
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
def main():
|
| 86 |
+
if len(sys.argv) < 2:
|
| 87 |
+
print("effect: None and std_error: None")
|
| 88 |
+
return
|
| 89 |
+
path = sys.argv[1]
|
| 90 |
+
try:
|
| 91 |
+
df = load_data(path)
|
| 92 |
+
except Exception:
|
| 93 |
+
print("effect: None and std_error: None")
|
| 94 |
+
return
|
| 95 |
+
events = identify_events(df)
|
| 96 |
+
# this script corresponds to estimation_3 (third event)
|
| 97 |
+
idx = 2
|
| 98 |
+
if idx >= len(events):
|
| 99 |
+
print("effect: None and std_error: None")
|
| 100 |
+
return
|
| 101 |
+
att, se = estimate_2x2_did(df, events[idx])
|
| 102 |
+
print(f"effect: {att} and std_error: {se}")
|
| 103 |
+
|
| 104 |
+
if __name__ == '__main__':
|
| 105 |
+
main()
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/estimation_4.py
ADDED
|
@@ -0,0 +1,105 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
import sys
|
| 3 |
+
import numpy as np
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import statsmodels.api as sm
|
| 6 |
+
|
| 7 |
+
def load_data(path):
|
| 8 |
+
df = pd.read_csv(path)
|
| 9 |
+
required_cols = ["country", "year", "polarization", "rtreatment"]
|
| 10 |
+
for col in required_cols:
|
| 11 |
+
if col not in df.columns:
|
| 12 |
+
raise ValueError(f"Missing required column: {col}")
|
| 13 |
+
df["country"] = df["country"].astype(str)
|
| 14 |
+
df["year"] = pd.to_numeric(df["year"], errors="coerce").astype("Int64")
|
| 15 |
+
df["rtreatment"] = pd.to_numeric(df["rtreatment"], errors="coerce")
|
| 16 |
+
df["polarization"] = pd.to_numeric(df["polarization"], errors="coerce")
|
| 17 |
+
df = df.dropna(subset=["country", "year", "rtreatment", "polarization"])
|
| 18 |
+
df["rtreatment"] = (df["rtreatment"] > 0).astype(int)
|
| 19 |
+
df = df.sort_values(["country", "year"]).drop_duplicates(["country", "year"])
|
| 20 |
+
return df
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
def identify_events(df):
|
| 24 |
+
events = []
|
| 25 |
+
for country, g in df.groupby("country"):
|
| 26 |
+
g = g.sort_values("year")
|
| 27 |
+
treated_years = g.loc[g["rtreatment"] == 1, "year"]
|
| 28 |
+
if treated_years.empty:
|
| 29 |
+
continue
|
| 30 |
+
t0 = int(treated_years.min())
|
| 31 |
+
has_pre_zero = ((g["year"] < t0) & (g["rtreatment"] == 0)).any()
|
| 32 |
+
if not has_pre_zero:
|
| 33 |
+
continue
|
| 34 |
+
pre_candidates = g[(g["year"] < t0) & (g["rtreatment"] == 0) & g["polarization"].notna()]
|
| 35 |
+
if pre_candidates.empty:
|
| 36 |
+
continue
|
| 37 |
+
pre_year = int(pre_candidates["year"].max())
|
| 38 |
+
post_candidates = g[(g["year"] >= t0) & (g["rtreatment"] == 1) & g["polarization"].notna()]
|
| 39 |
+
if post_candidates.empty:
|
| 40 |
+
continue
|
| 41 |
+
post_year = int(post_candidates["year"].min())
|
| 42 |
+
controls = []
|
| 43 |
+
for c2, g2 in df.groupby("country"):
|
| 44 |
+
if c2 == country:
|
| 45 |
+
continue
|
| 46 |
+
g2_pre = g2[g2["year"] == pre_year]
|
| 47 |
+
g2_post = g2[g2["year"] == post_year]
|
| 48 |
+
if len(g2_pre) == 1 and len(g2_post) == 1:
|
| 49 |
+
if (g2_pre["rtreatment"].iloc[0] == 0) and (g2_post["rtreatment"].iloc[0] == 0):
|
| 50 |
+
if np.isfinite(g2_pre["polarization"].iloc[0]) and np.isfinite(g2_post["polarization"].iloc[0]):
|
| 51 |
+
controls.append(c2)
|
| 52 |
+
if len(controls) >= 2:
|
| 53 |
+
events.append({
|
| 54 |
+
"country": country,
|
| 55 |
+
"t0": t0,
|
| 56 |
+
"pre_year": pre_year,
|
| 57 |
+
"post_year": post_year,
|
| 58 |
+
"controls": controls,
|
| 59 |
+
"n_controls": len(controls)
|
| 60 |
+
})
|
| 61 |
+
events.sort(key=lambda x: (-x["n_controls"], x["post_year"]))
|
| 62 |
+
return events
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
def estimate_2x2_did(df, event):
|
| 66 |
+
treated = event["country"]
|
| 67 |
+
pre_year = event["pre_year"]
|
| 68 |
+
post_year = event["post_year"]
|
| 69 |
+
controls = event["controls"]
|
| 70 |
+
subset = df[(df["year"].isin([pre_year, post_year])) & (df["country"].isin([treated] + controls))].copy()
|
| 71 |
+
subset["post"] = (subset["year"] == post_year).astype(int)
|
| 72 |
+
subset["treated_group"] = (subset["country"] == treated).astype(int)
|
| 73 |
+
subset["did"] = subset["post"] * subset["treated_group"]
|
| 74 |
+
X = subset[["treated_group", "post", "did"]]
|
| 75 |
+
X = sm.add_constant(X)
|
| 76 |
+
y = subset["polarization"].values
|
| 77 |
+
groups = subset["country"].astype("category").cat.codes.values
|
| 78 |
+
model = sm.OLS(y, X)
|
| 79 |
+
res = model.fit(cov_type='cluster', cov_kwds={'groups': groups})
|
| 80 |
+
att = res.params.get("did", np.nan)
|
| 81 |
+
se = res.bse.get("did", np.nan)
|
| 82 |
+
return (None if not np.isfinite(att) else float(att), None if not np.isfinite(se) else float(se))
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
def main():
|
| 86 |
+
if len(sys.argv) < 2:
|
| 87 |
+
print("effect: None and std_error: None")
|
| 88 |
+
return
|
| 89 |
+
path = sys.argv[1]
|
| 90 |
+
try:
|
| 91 |
+
df = load_data(path)
|
| 92 |
+
except Exception:
|
| 93 |
+
print("effect: None and std_error: None")
|
| 94 |
+
return
|
| 95 |
+
events = identify_events(df)
|
| 96 |
+
# this script corresponds to estimation_4 (fourth event)
|
| 97 |
+
idx = 3
|
| 98 |
+
if idx >= len(events):
|
| 99 |
+
print("effect: None and std_error: None")
|
| 100 |
+
return
|
| 101 |
+
att, se = estimate_2x2_did(df, events[idx])
|
| 102 |
+
print(f"effect: {att} and std_error: {se}")
|
| 103 |
+
|
| 104 |
+
if __name__ == '__main__':
|
| 105 |
+
main()
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/estimation_5.py
ADDED
|
@@ -0,0 +1,105 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
import sys
|
| 3 |
+
import numpy as np
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import statsmodels.api as sm
|
| 6 |
+
|
| 7 |
+
def load_data(path):
|
| 8 |
+
df = pd.read_csv(path)
|
| 9 |
+
required_cols = ["country", "year", "polarization", "rtreatment"]
|
| 10 |
+
for col in required_cols:
|
| 11 |
+
if col not in df.columns:
|
| 12 |
+
raise ValueError(f"Missing required column: {col}")
|
| 13 |
+
df["country"] = df["country"].astype(str)
|
| 14 |
+
df["year"] = pd.to_numeric(df["year"], errors="coerce").astype("Int64")
|
| 15 |
+
df["rtreatment"] = pd.to_numeric(df["rtreatment"], errors="coerce")
|
| 16 |
+
df["polarization"] = pd.to_numeric(df["polarization"], errors="coerce")
|
| 17 |
+
df = df.dropna(subset=["country", "year", "rtreatment", "polarization"])
|
| 18 |
+
df["rtreatment"] = (df["rtreatment"] > 0).astype(int)
|
| 19 |
+
df = df.sort_values(["country", "year"]).drop_duplicates(["country", "year"])
|
| 20 |
+
return df
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
def identify_events(df):
|
| 24 |
+
events = []
|
| 25 |
+
for country, g in df.groupby("country"):
|
| 26 |
+
g = g.sort_values("year")
|
| 27 |
+
treated_years = g.loc[g["rtreatment"] == 1, "year"]
|
| 28 |
+
if treated_years.empty:
|
| 29 |
+
continue
|
| 30 |
+
t0 = int(treated_years.min())
|
| 31 |
+
has_pre_zero = ((g["year"] < t0) & (g["rtreatment"] == 0)).any()
|
| 32 |
+
if not has_pre_zero:
|
| 33 |
+
continue
|
| 34 |
+
pre_candidates = g[(g["year"] < t0) & (g["rtreatment"] == 0) & g["polarization"].notna()]
|
| 35 |
+
if pre_candidates.empty:
|
| 36 |
+
continue
|
| 37 |
+
pre_year = int(pre_candidates["year"].max())
|
| 38 |
+
post_candidates = g[(g["year"] >= t0) & (g["rtreatment"] == 1) & g["polarization"].notna()]
|
| 39 |
+
if post_candidates.empty:
|
| 40 |
+
continue
|
| 41 |
+
post_year = int(post_candidates["year"].min())
|
| 42 |
+
controls = []
|
| 43 |
+
for c2, g2 in df.groupby("country"):
|
| 44 |
+
if c2 == country:
|
| 45 |
+
continue
|
| 46 |
+
g2_pre = g2[g2["year"] == pre_year]
|
| 47 |
+
g2_post = g2[g2["year"] == post_year]
|
| 48 |
+
if len(g2_pre) == 1 and len(g2_post) == 1:
|
| 49 |
+
if (g2_pre["rtreatment"].iloc[0] == 0) and (g2_post["rtreatment"].iloc[0] == 0):
|
| 50 |
+
if np.isfinite(g2_pre["polarization"].iloc[0]) and np.isfinite(g2_post["polarization"].iloc[0]):
|
| 51 |
+
controls.append(c2)
|
| 52 |
+
if len(controls) >= 2:
|
| 53 |
+
events.append({
|
| 54 |
+
"country": country,
|
| 55 |
+
"t0": t0,
|
| 56 |
+
"pre_year": pre_year,
|
| 57 |
+
"post_year": post_year,
|
| 58 |
+
"controls": controls,
|
| 59 |
+
"n_controls": len(controls)
|
| 60 |
+
})
|
| 61 |
+
events.sort(key=lambda x: (-x["n_controls"], x["post_year"]))
|
| 62 |
+
return events
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
def estimate_2x2_did(df, event):
|
| 66 |
+
treated = event["country"]
|
| 67 |
+
pre_year = event["pre_year"]
|
| 68 |
+
post_year = event["post_year"]
|
| 69 |
+
controls = event["controls"]
|
| 70 |
+
subset = df[(df["year"].isin([pre_year, post_year])) & (df["country"].isin([treated] + controls))].copy()
|
| 71 |
+
subset["post"] = (subset["year"] == post_year).astype(int)
|
| 72 |
+
subset["treated_group"] = (subset["country"] == treated).astype(int)
|
| 73 |
+
subset["did"] = subset["post"] * subset["treated_group"]
|
| 74 |
+
X = subset[["treated_group", "post", "did"]]
|
| 75 |
+
X = sm.add_constant(X)
|
| 76 |
+
y = subset["polarization"].values
|
| 77 |
+
groups = subset["country"].astype("category").cat.codes.values
|
| 78 |
+
model = sm.OLS(y, X)
|
| 79 |
+
res = model.fit(cov_type='cluster', cov_kwds={'groups': groups})
|
| 80 |
+
att = res.params.get("did", np.nan)
|
| 81 |
+
se = res.bse.get("did", np.nan)
|
| 82 |
+
return (None if not np.isfinite(att) else float(att), None if not np.isfinite(se) else float(se))
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
def main():
|
| 86 |
+
if len(sys.argv) < 2:
|
| 87 |
+
print("effect: None and std_error: None")
|
| 88 |
+
return
|
| 89 |
+
path = sys.argv[1]
|
| 90 |
+
try:
|
| 91 |
+
df = load_data(path)
|
| 92 |
+
except Exception:
|
| 93 |
+
print("effect: None and std_error: None")
|
| 94 |
+
return
|
| 95 |
+
events = identify_events(df)
|
| 96 |
+
# this script corresponds to estimation_5 (fifth event)
|
| 97 |
+
idx = 4
|
| 98 |
+
if idx >= len(events):
|
| 99 |
+
print("effect: None and std_error: None")
|
| 100 |
+
return
|
| 101 |
+
att, se = estimate_2x2_did(df, events[idx])
|
| 102 |
+
print(f"effect: {att} and std_error: {se}")
|
| 103 |
+
|
| 104 |
+
if __name__ == '__main__':
|
| 105 |
+
main()
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/output_1.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
effect: 0.09729280255057204 and std_error: 0.028400841127127093
|
| 2 |
+
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/output_2.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
effect: 0.08769751787185631 and std_error: 0.029867683613416504
|
| 2 |
+
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/output_3.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
effect: -0.04024610254499533 and std_error: 0.04400501181021188
|
| 2 |
+
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/output_4.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
effect: 0.09716198179456897 and std_error: 0.028625018041939405
|
| 2 |
+
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/estimation/output_5.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
effect: 0.09098988109164768 and std_error: 0.0385379806528948
|
| 2 |
+
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/finding_1.json
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"identification_strategy": {
|
| 3 |
+
"strategy": "Difference-in-Differences",
|
| 4 |
+
"variant": "sharp 2x2 (single-cohort)",
|
| 5 |
+
"treatments": [
|
| 6 |
+
"rtreatment"
|
| 7 |
+
],
|
| 8 |
+
"outcomes": [
|
| 9 |
+
"polarization"
|
| 10 |
+
],
|
| 11 |
+
"outcome_is_stacked": false,
|
| 12 |
+
"controls": null,
|
| 13 |
+
"post_treatment_variables": null,
|
| 14 |
+
"minimal_controlling_set": null,
|
| 15 |
+
"reason_for_minimal_controlling_set": null,
|
| 16 |
+
"time_variable": "year",
|
| 17 |
+
"group_variable": "country"
|
| 18 |
+
},
|
| 19 |
+
"quantity": "ATT",
|
| 20 |
+
"estimand_population": "Treated cohort: Bulgaria; Controls: not-yet-treated in 2004 and 2005",
|
| 21 |
+
"quantity_value": 0.09729280255057204,
|
| 22 |
+
"quantity_ci": {
|
| 23 |
+
"lower": 0.04162715394140294,
|
| 24 |
+
"upper": 0.15295845115974116,
|
| 25 |
+
"level": 0.95
|
| 26 |
+
},
|
| 27 |
+
"standard_error": 0.028400841127127093,
|
| 28 |
+
"p_value": 0.0006132140143484859,
|
| 29 |
+
"effect_units": "polarization (std. dev. of left-right placements)",
|
| 30 |
+
"subgroup": null,
|
| 31 |
+
"exact_causal_question": "ATT of radical-right party entry (rtreatment) on polarization using a 2x2 DiD: treated cohort = Bulgaria with pre = 2004 and post = 2005; controls = countries with rtreatment = 0 in both years.",
|
| 32 |
+
"layman_query": "Did polarization change in Bulgaria after its first radical-right entry compared to similar countries that had not yet experienced such entry, from 2004 to 2005?"
|
| 33 |
+
}
|
repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/finding_2.json
ADDED
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@@ -0,0 +1,33 @@
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| 1 |
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 32 |
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| 33 |
+
}
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repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/finding_3.json
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@@ -0,0 +1,33 @@
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|
| 32 |
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|
| 33 |
+
}
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repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/finding_4.json
ADDED
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@@ -0,0 +1,33 @@
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| 32 |
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| 33 |
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}
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repo-type=dataset/research_papers/DiD/Bischof_Wagner_2019/finding_5.json
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|
| 1 |
+
income,educationClean,sex,age,partytimeinvar,partycue,id,time,bi,treatment
|
| 2 |
+
700.000 eller derover,5.0,1.0,35,1.0,0.0,6,2,1.0,0.0
|
| 3 |
+
600.000 - 699.999 kr.,5.0,1.0,57,0.0,0.0,10,2,0.5,0.0
|
| 4 |
+
300.000 - 399.999 kr.,4.0,1.0,42,1.0,0.0,12,2,0.5,0.0
|
| 5 |
+
700.000 eller derover,5.0,1.0,39,0.0,0.0,15,2,0.5,0.0
|
| 6 |
+
500.000 - 599.999 kr.,4.0,0.0,41,,0.0,16,2,0.75,
|
| 7 |
+
Onsker ikke at oplyse,2.0,1.0,47,,0.0,24,2,0.25,
|
| 8 |
+
Onsker ikke at oplyse,3.0,0.0,57,,0.0,27,2,0.5,
|
| 9 |
+
300.000 - 399.999 kr.,4.0,0.0,59,0.0,0.0,31,2,0.5,0.0
|
| 10 |
+
600.000 - 699.999 kr.,2.0,1.0,42,,0.0,32,2,0.25,
|
| 11 |
+
Onsker ikke at oplyse,4.0,1.0,62,0.0,0.0,33,2,0.5,0.0
|
| 12 |
+
Onsker ikke at oplyse,3.0,1.0,61,0.0,0.0,40,2,0.75,0.0
|
| 13 |
+
700.000 eller derover,4.0,0.0,48,0.0,0.0,42,2,0.75,0.0
|
| 14 |
+
700.000 eller derover,4.0,0.0,54,1.0,0.0,44,2,0.75,0.0
|
| 15 |
+
700.000 eller derover,4.0,1.0,55,,0.0,48,2,0.5,
|
| 16 |
+
Onsker ikke at oplyse,3.0,1.0,23,1.0,0.0,50,2,0.5,0.0
|
| 17 |
+
Indtil 99.999 kr.,3.0,1.0,25,,0.0,52,2,0.75,
|
| 18 |
+
700.000 eller derover,3.0,1.0,45,0.0,0.0,55,2,0.5,0.0
|
| 19 |
+
500.000 - 599.999 kr.,5.0,0.0,38,1.0,0.0,59,2,0.75,0.0
|
| 20 |
+
300.000 - 399.999 kr.,4.0,0.0,31,0.0,0.0,64,2,0.5,0.0
|
| 21 |
+
500.000 - 599.999 kr.,5.0,1.0,53,0.0,0.0,65,2,0.5,0.0
|
| 22 |
+
200.000 - 299.999 kr.,3.0,1.0,42,,0.0,69,2,0.75,
|
| 23 |
+
700.000 eller derover,4.0,0.0,42,1.0,0.0,70,2,0.5,0.0
|
| 24 |
+
300.000 - 399.999 kr.,1.0,1.0,45,,0.0,72,2,1.0,
|
| 25 |
+
700.000 eller derover,5.0,0.0,42,,0.0,77,2,0.5,
|
| 26 |
+
600.000 - 699.999 kr.,4.0,1.0,64,1.0,0.0,79,2,1.0,0.0
|
| 27 |
+
700.000 eller derover,5.0,1.0,41,,0.0,81,2,0.75,
|
| 28 |
+
600.000 - 699.999 kr.,4.0,1.0,57,1.0,0.0,85,2,0.25,0.0
|
| 29 |
+
600.000 - 699.999 kr.,2.0,1.0,60,0.0,0.0,87,2,0.5,0.0
|
| 30 |
+
600.000 - 699.999 kr.,5.0,1.0,33,,0.0,89,2,0.5,
|
| 31 |
+
400.000 - 499.999 kr.,4.0,0.0,67,1.0,0.0,90,2,0.75,0.0
|
| 32 |
+
700.000 eller derover,4.0,1.0,63,1.0,0.0,92,2,0.75,0.0
|
| 33 |
+
400.000 - 499.999 kr.,2.0,1.0,54,1.0,0.0,95,2,1.0,0.0
|
| 34 |
+
Onsker ikke at oplyse,5.0,1.0,49,0.0,0.0,98,2,0.5,0.0
|
| 35 |
+
700.000 eller derover,4.0,1.0,51,1.0,0.0,101,2,0.25,0.0
|
| 36 |
+
500.000 - 599.999 kr.,5.0,1.0,84,,0.0,104,2,0.5,
|
| 37 |
+
200.000 - 299.999 kr.,3.0,0.0,45,0.0,0.0,106,2,0.5,0.0
|
| 38 |
+
600.000 - 699.999 kr.,1.0,1.0,59,1.0,0.0,110,2,0.25,0.0
|
| 39 |
+
Onsker ikke at oplyse,4.0,0.0,63,1.0,0.0,114,2,0.5,0.0
|
| 40 |
+
600.000 - 699.999 kr.,5.0,1.0,63,,0.0,121,2,0.5,
|
| 41 |
+
400.000 - 499.999 kr.,4.0,1.0,55,1.0,0.0,123,2,0.5,0.0
|
| 42 |
+
200.000 - 299.999 kr.,4.0,0.0,41,,0.0,124,2,1.0,
|
| 43 |
+
600.000 - 699.999 kr.,5.0,1.0,35,,0.0,129,2,0.75,
|
| 44 |
+
200.000 - 299.999 kr.,4.0,1.0,37,1.0,0.0,131,2,0.75,0.0
|
| 45 |
+
400.000 - 499.999 kr.,2.0,1.0,51,0.0,0.0,132,2,1.0,0.0
|
| 46 |
+
100.000 - 199.999 kr.,3.0,0.0,48,,0.0,142,2,0.75,
|
| 47 |
+
400.000 - 499.999 kr.,1.0,1.0,62,,0.0,144,2,0.5,
|
| 48 |
+
100.000 - 199.999 kr.,1.0,1.0,64,0.0,0.0,145,2,0.25,0.0
|
| 49 |
+
500.000 - 599.999 kr.,4.0,1.0,66,,0.0,146,2,0.25,
|
| 50 |
+
200.000 - 299.999 kr.,4.0,0.0,33,,0.0,150,2,0.75,
|
| 51 |
+
100.000 - 199.999 kr.,4.0,1.0,21,1.0,0.0,151,2,0.75,0.0
|
| 52 |
+
400.000 - 499.999 kr.,5.0,1.0,61,1.0,0.0,174,2,0.75,0.0
|
| 53 |
+
300.000 - 399.999 kr.,2.0,0.0,51,1.0,0.0,175,2,0.5,0.0
|
| 54 |
+
700.000 eller derover,4.0,1.0,45,0.0,0.0,178,2,0.5,0.0
|
| 55 |
+
700.000 eller derover,4.0,1.0,63,0.0,0.0,180,2,0.5,0.0
|
| 56 |
+
400.000 - 499.999 kr.,2.0,0.0,46,1.0,0.0,184,2,0.75,0.0
|
| 57 |
+
600.000 - 699.999 kr.,4.0,1.0,34,,0.0,185,2,0.5,
|
| 58 |
+
300.000 - 399.999 kr.,4.0,1.0,28,1.0,0.0,191,2,0.75,0.0
|
| 59 |
+
700.000 eller derover,5.0,1.0,62,,0.0,199,2,0.25,
|
| 60 |
+
300.000 - 399.999 kr.,4.0,0.0,71,0.0,0.0,200,2,0.75,0.0
|
| 61 |
+
700.000 eller derover,5.0,1.0,49,0.0,0.0,202,2,0.75,0.0
|
| 62 |
+
300.000 - 399.999 kr.,4.0,0.0,56,,0.0,204,2,0.25,
|
| 63 |
+
500.000 - 599.999 kr.,4.0,0.0,53,,0.0,209,2,0.5,
|
| 64 |
+
700.000 eller derover,4.0,1.0,58,1.0,0.0,215,2,0.25,0.0
|
| 65 |
+
700.000 eller derover,4.0,1.0,37,,0.0,217,2,0.75,
|
| 66 |
+
Onsker ikke at oplyse,2.0,1.0,56,0.0,0.0,228,2,0.75,0.0
|
| 67 |
+
300.000 - 399.999 kr.,5.0,1.0,29,,0.0,234,2,1.0,
|
| 68 |
+
400.000 - 499.999 kr.,2.0,1.0,45,1.0,0.0,237,2,0.75,0.0
|
| 69 |
+
400.000 - 499.999 kr.,2.0,1.0,61,0.0,0.0,250,2,0.5,0.0
|
| 70 |
+
Onsker ikke at oplyse,4.0,1.0,67,,0.0,251,2,0.0,
|
| 71 |
+
700.000 eller derover,4.0,1.0,47,0.0,0.0,252,2,0.75,0.0
|
| 72 |
+
600.000 - 699.999 kr.,4.0,1.0,57,,0.0,257,2,0.75,
|
| 73 |
+
500.000 - 599.999 kr.,4.0,1.0,51,,0.0,262,2,1.0,
|
| 74 |
+
700.000 eller derover,5.0,0.0,30,1.0,0.0,264,2,0.5,0.0
|
| 75 |
+
700.000 eller derover,5.0,1.0,35,0.0,0.0,270,2,0.5,0.0
|
| 76 |
+
700.000 eller derover,2.0,1.0,55,0.0,0.0,271,2,0.5,0.0
|
| 77 |
+
500.000 - 599.999 kr.,2.0,0.0,43,,0.0,274,2,0.75,
|
| 78 |
+
600.000 - 699.999 kr.,5.0,0.0,40,1.0,0.0,283,2,0.5,0.0
|
| 79 |
+
700.000 eller derover,4.0,0.0,54,1.0,0.0,288,2,0.5,0.0
|
| 80 |
+
400.000 - 499.999 kr.,4.0,0.0,47,1.0,0.0,291,2,0.75,0.0
|
| 81 |
+
300.000 - 399.999 kr.,5.0,1.0,28,,0.0,293,2,1.0,
|
| 82 |
+
400.000 - 499.999 kr.,4.0,1.0,43,1.0,0.0,296,2,0.75,0.0
|
| 83 |
+
700.000 eller derover,2.0,0.0,45,,0.0,300,2,0.5,
|
| 84 |
+
300.000 - 399.999 kr.,4.0,0.0,34,1.0,0.0,304,2,0.75,0.0
|
| 85 |
+
600.000 - 699.999 kr.,5.0,1.0,69,1.0,0.0,306,2,1.0,0.0
|
| 86 |
+
500.000 - 599.999 kr.,4.0,0.0,42,1.0,0.0,307,2,0.75,0.0
|
| 87 |
+
400.000 - 499.999 kr.,2.0,1.0,47,1.0,0.0,308,2,0.5,0.0
|
| 88 |
+
Onsker ikke at oplyse,3.0,0.0,41,0.0,0.0,309,2,0.75,0.0
|
| 89 |
+
700.000 eller derover,1.0,0.0,58,0.0,0.0,311,2,0.75,0.0
|
| 90 |
+
600.000 - 699.999 kr.,4.0,0.0,51,1.0,0.0,314,2,0.5,0.0
|
| 91 |
+
300.000 - 399.999 kr.,2.0,1.0,64,,0.0,316,2,0.75,
|
| 92 |
+
100.000 - 199.999 kr.,5.0,1.0,51,,0.0,318,2,0.25,
|
| 93 |
+
500.000 - 599.999 kr.,3.0,1.0,43,0.0,0.0,323,2,0.75,0.0
|
| 94 |
+
400.000 - 499.999 kr.,2.0,1.0,60,,0.0,325,2,0.75,
|
| 95 |
+
200.000 - 299.999 kr.,1.0,1.0,49,,0.0,326,2,1.0,
|
| 96 |
+
200.000 - 299.999 kr.,4.0,0.0,55,0.0,0.0,327,2,0.5,0.0
|
| 97 |
+
600.000 - 699.999 kr.,4.0,0.0,55,,0.0,329,2,0.75,
|
| 98 |
+
700.000 eller derover,4.0,1.0,46,,0.0,344,2,0.25,
|
| 99 |
+
600.000 - 699.999 kr.,2.0,0.0,43,1.0,0.0,346,2,0.25,0.0
|
| 100 |
+
700.000 eller derover,5.0,1.0,49,,0.0,348,2,1.0,
|
| 101 |
+
700.000 eller derover,5.0,1.0,63,0.0,0.0,354,2,0.75,0.0
|
| 102 |
+
700.000 eller derover,3.0,1.0,51,1.0,0.0,358,2,0.25,0.0
|
| 103 |
+
500.000 - 599.999 kr.,2.0,1.0,50,1.0,0.0,366,2,0.75,0.0
|
| 104 |
+
700.000 eller derover,4.0,0.0,39,0.0,0.0,370,2,0.5,0.0
|
| 105 |
+
200.000 - 299.999 kr.,2.0,0.0,50,,0.0,374,2,0.5,
|
| 106 |
+
400.000 - 499.999 kr.,5.0,0.0,62,1.0,0.0,376,2,0.75,0.0
|
| 107 |
+
500.000 - 599.999 kr.,5.0,1.0,51,,0.0,379,2,0.25,
|
| 108 |
+
300.000 - 399.999 kr.,1.0,1.0,56,,0.0,381,2,0.5,
|
| 109 |
+
400.000 - 499.999 kr.,2.0,1.0,60,0.0,0.0,392,2,0.5,0.0
|
| 110 |
+
500.000 - 599.999 kr.,4.0,0.0,60,,0.0,393,2,0.75,
|
| 111 |
+
200.000 - 299.999 kr.,4.0,1.0,78,1.0,0.0,399,2,0.75,0.0
|
| 112 |
+
600.000 - 699.999 kr.,2.0,1.0,55,,0.0,407,2,0.75,
|
| 113 |
+
700.000 eller derover,5.0,0.0,64,1.0,0.0,413,2,0.5,0.0
|
| 114 |
+
600.000 - 699.999 kr.,2.0,1.0,52,0.0,0.0,428,2,0.5,0.0
|
| 115 |
+
500.000 - 599.999 kr.,2.0,1.0,59,1.0,0.0,431,2,0.75,0.0
|
| 116 |
+
700.000 eller derover,5.0,1.0,48,0.0,0.0,432,2,0.5,0.0
|
| 117 |
+
600.000 - 699.999 kr.,3.0,1.0,55,1.0,0.0,433,2,0.75,0.0
|
| 118 |
+
700.000 eller derover,5.0,1.0,38,1.0,0.0,436,2,0.75,0.0
|
| 119 |
+
700.000 eller derover,4.0,1.0,48,1.0,0.0,438,2,1.0,0.0
|
| 120 |
+
100.000 - 199.999 kr.,5.0,0.0,25,,0.0,441,2,1.0,
|
| 121 |
+
Indtil 99.999 kr.,4.0,1.0,36,0.0,0.0,444,2,0.5,0.0
|
| 122 |
+
700.000 eller derover,5.0,1.0,60,,0.0,446,2,0.5,
|
| 123 |
+
200.000 - 299.999 kr.,2.0,1.0,48,1.0,0.0,447,2,0.75,0.0
|
| 124 |
+
500.000 - 599.999 kr.,1.0,0.0,61,,0.0,453,2,0.75,
|
| 125 |
+
700.000 eller derover,4.0,1.0,36,0.0,0.0,461,2,0.5,0.0
|
| 126 |
+
200.000 - 299.999 kr.,4.0,0.0,60,,0.0,467,2,0.5,
|
| 127 |
+
500.000 - 599.999 kr.,1.0,0.0,55,,0.0,468,2,0.75,
|
| 128 |
+
300.000 - 399.999 kr.,4.0,1.0,53,1.0,0.0,470,2,0.5,0.0
|
| 129 |
+
600.000 - 699.999 kr.,5.0,0.0,34,0.0,0.0,473,2,0.25,0.0
|
| 130 |
+
300.000 - 399.999 kr.,4.0,0.0,48,0.0,0.0,474,2,0.5,0.0
|
| 131 |
+
600.000 - 699.999 kr.,2.0,0.0,32,0.0,0.0,478,2,0.25,0.0
|
| 132 |
+
500.000 - 599.999 kr.,4.0,0.0,51,0.0,0.0,480,2,0.5,0.0
|
| 133 |
+
400.000 - 499.999 kr.,4.0,1.0,68,1.0,0.0,483,2,0.75,0.0
|
| 134 |
+
600.000 - 699.999 kr.,3.0,1.0,29,0.0,0.0,485,2,0.5,0.0
|
| 135 |
+
700.000 eller derover,5.0,1.0,61,,0.0,488,2,0.25,
|
| 136 |
+
100.000 - 199.999 kr.,1.0,1.0,48,1.0,0.0,490,2,0.75,0.0
|
| 137 |
+
500.000 - 599.999 kr.,5.0,1.0,59,0.0,0.0,499,2,0.5,0.0
|
| 138 |
+
300.000 - 399.999 kr.,4.0,0.0,32,,0.0,503,2,0.5,
|
| 139 |
+
500.000 - 599.999 kr.,4.0,1.0,56,0.0,0.0,505,2,0.5,0.0
|
| 140 |
+
300.000 - 399.999 kr.,4.0,0.0,64,1.0,0.0,507,2,0.75,0.0
|
| 141 |
+
700.000 eller derover,5.0,1.0,34,0.0,0.0,509,2,0.0,0.0
|
| 142 |
+
500.000 - 599.999 kr.,4.0,1.0,50,,0.0,510,2,0.5,
|
| 143 |
+
300.000 - 399.999 kr.,1.0,1.0,64,,0.0,515,2,0.5,
|
| 144 |
+
300.000 - 399.999 kr.,4.0,0.0,51,,0.0,517,2,1.0,
|
| 145 |
+
700.000 eller derover,4.0,1.0,46,0.0,0.0,528,2,0.75,0.0
|
| 146 |
+
Onsker ikke at oplyse,4.0,1.0,67,0.0,0.0,529,2,0.25,0.0
|
| 147 |
+
700.000 eller derover,5.0,1.0,61,1.0,0.0,532,2,1.0,0.0
|
| 148 |
+
300.000 - 399.999 kr.,2.0,1.0,56,1.0,0.0,533,2,0.25,0.0
|
| 149 |
+
400.000 - 499.999 kr.,1.0,0.0,21,1.0,0.0,537,2,0.75,0.0
|
| 150 |
+
400.000 - 499.999 kr.,2.0,0.0,60,,0.0,539,2,0.5,
|
| 151 |
+
400.000 - 499.999 kr.,4.0,1.0,64,1.0,0.0,544,2,0.5,0.0
|
| 152 |
+
600.000 - 699.999 kr.,2.0,0.0,54,,0.0,545,2,0.75,
|
| 153 |
+
500.000 - 599.999 kr.,1.0,1.0,48,1.0,0.0,546,2,0.0,0.0
|
| 154 |
+
300.000 - 399.999 kr.,2.0,1.0,59,1.0,0.0,551,2,0.75,0.0
|
| 155 |
+
600.000 - 699.999 kr.,4.0,1.0,51,1.0,0.0,553,2,0.75,0.0
|
| 156 |
+
700.000 eller derover,2.0,1.0,50,0.0,0.0,554,2,0.25,0.0
|
| 157 |
+
500.000 - 599.999 kr.,4.0,1.0,46,,0.0,558,2,0.25,
|
| 158 |
+
100.000 - 199.999 kr.,4.0,1.0,41,,0.0,560,2,0.0,
|
| 159 |
+
700.000 eller derover,4.0,1.0,54,0.0,0.0,561,2,0.5,0.0
|
| 160 |
+
Onsker ikke at oplyse,2.0,1.0,45,,0.0,562,2,0.5,
|
| 161 |
+
600.000 - 699.999 kr.,4.0,1.0,57,,0.0,565,2,0.25,
|
| 162 |
+
Onsker ikke at oplyse,4.0,1.0,62,,0.0,567,2,0.75,
|
| 163 |
+
Onsker ikke at oplyse,2.0,0.0,43,0.0,0.0,575,2,0.25,0.0
|
| 164 |
+
Onsker ikke at oplyse,2.0,0.0,54,1.0,0.0,576,2,0.75,0.0
|
| 165 |
+
Onsker ikke at oplyse,4.0,1.0,70,0.0,0.0,577,2,0.5,0.0
|
| 166 |
+
600.000 - 699.999 kr.,4.0,0.0,52,1.0,0.0,580,2,0.5,0.0
|
| 167 |
+
500.000 - 599.999 kr.,5.0,0.0,55,1.0,0.0,584,2,0.5,0.0
|
| 168 |
+
700.000 eller derover,4.0,0.0,50,0.0,0.0,587,2,0.5,0.0
|
| 169 |
+
100.000 - 199.999 kr.,3.0,0.0,25,1.0,0.0,592,2,0.5,0.0
|
| 170 |
+
500.000 - 599.999 kr.,2.0,0.0,39,1.0,0.0,602,2,0.25,0.0
|
| 171 |
+
700.000 eller derover,4.0,0.0,57,1.0,0.0,608,2,1.0,0.0
|
| 172 |
+
500.000 - 599.999 kr.,2.0,1.0,37,0.0,0.0,609,2,0.75,0.0
|
| 173 |
+
300.000 - 399.999 kr.,1.0,1.0,65,0.0,0.0,611,2,0.75,0.0
|
| 174 |
+
Onsker ikke at oplyse,4.0,1.0,66,1.0,0.0,612,2,0.25,0.0
|
| 175 |
+
700.000 eller derover,4.0,0.0,41,0.0,0.0,615,2,0.25,0.0
|
| 176 |
+
500.000 - 599.999 kr.,4.0,1.0,43,0.0,0.0,617,2,0.75,0.0
|
| 177 |
+
300.000 - 399.999 kr.,2.0,0.0,44,1.0,0.0,620,2,0.75,0.0
|
| 178 |
+
300.000 - 399.999 kr.,2.0,1.0,44,0.0,0.0,622,2,0.5,0.0
|
| 179 |
+
Onsker ikke at oplyse,5.0,1.0,54,1.0,0.0,623,2,0.25,0.0
|
| 180 |
+
Onsker ikke at oplyse,4.0,1.0,61,0.0,0.0,625,2,0.5,0.0
|
| 181 |
+
500.000 - 599.999 kr.,2.0,0.0,53,0.0,0.0,626,2,0.5,0.0
|
| 182 |
+
400.000 - 499.999 kr.,4.0,0.0,61,1.0,0.0,627,2,0.75,0.0
|
| 183 |
+
300.000 - 399.999 kr.,5.0,0.0,29,,0.0,630,2,0.25,
|
| 184 |
+
Onsker ikke at oplyse,4.0,0.0,48,0.0,0.0,634,2,0.5,0.0
|
| 185 |
+
700.000 eller derover,5.0,0.0,43,,0.0,637,2,1.0,
|
| 186 |
+
700.000 eller derover,4.0,1.0,47,0.0,0.0,641,2,0.5,0.0
|
| 187 |
+
100.000 - 199.999 kr.,4.0,0.0,55,,0.0,647,2,0.5,
|
| 188 |
+
100.000 - 199.999 kr.,5.0,1.0,58,,0.0,648,2,0.5,
|
| 189 |
+
300.000 - 399.999 kr.,3.0,1.0,48,,0.0,649,2,0.5,
|
| 190 |
+
300.000 - 399.999 kr.,3.0,1.0,51,1.0,0.0,655,2,0.25,0.0
|
| 191 |
+
Indtil 99.999 kr.,4.0,0.0,44,1.0,0.0,657,2,0.5,0.0
|
| 192 |
+
200.000 - 299.999 kr.,2.0,1.0,64,,0.0,659,2,0.25,
|
| 193 |
+
700.000 eller derover,4.0,1.0,56,,0.0,660,2,0.5,
|
| 194 |
+
500.000 - 599.999 kr.,3.0,0.0,43,0.0,0.0,661,2,0.5,0.0
|
| 195 |
+
500.000 - 599.999 kr.,3.0,0.0,27,,0.0,665,2,0.75,
|
| 196 |
+
500.000 - 599.999 kr.,5.0,1.0,71,,0.0,666,2,0.75,
|
| 197 |
+
600.000 - 699.999 kr.,2.0,0.0,36,0.0,0.0,679,2,0.75,0.0
|
| 198 |
+
100.000 - 199.999 kr.,4.0,0.0,25,1.0,0.0,692,2,0.5,0.0
|
| 199 |
+
600.000 - 699.999 kr.,4.0,0.0,48,1.0,0.0,696,2,1.0,0.0
|
| 200 |
+
200.000 - 299.999 kr.,4.0,1.0,47,,0.0,697,2,1.0,
|
| 201 |
+
600.000 - 699.999 kr.,5.0,1.0,63,1.0,0.0,702,2,0.75,0.0
|
| 202 |
+
300.000 - 399.999 kr.,2.0,1.0,67,,0.0,705,2,0.25,
|
| 203 |
+
300.000 - 399.999 kr.,4.0,1.0,67,0.0,0.0,708,2,0.5,0.0
|
| 204 |
+
700.000 eller derover,5.0,0.0,51,0.0,0.0,710,2,1.0,0.0
|
| 205 |
+
300.000 - 399.999 kr.,2.0,1.0,66,1.0,0.0,711,2,0.5,0.0
|
| 206 |
+
300.000 - 399.999 kr.,3.0,0.0,48,,0.0,713,2,0.25,
|
| 207 |
+
Onsker ikke at oplyse,4.0,1.0,57,0.0,0.0,714,2,0.5,0.0
|
| 208 |
+
200.000 - 299.999 kr.,4.0,0.0,61,1.0,0.0,717,2,0.75,0.0
|
| 209 |
+
300.000 - 399.999 kr.,4.0,1.0,69,0.0,0.0,718,2,0.75,0.0
|
| 210 |
+
700.000 eller derover,2.0,1.0,53,0.0,0.0,720,2,0.5,0.0
|
| 211 |
+
700.000 eller derover,2.0,1.0,61,0.0,0.0,721,2,0.25,0.0
|
| 212 |
+
300.000 - 399.999 kr.,4.0,1.0,46,1.0,0.0,723,2,0.75,0.0
|
| 213 |
+
500.000 - 599.999 kr.,4.0,1.0,64,0.0,0.0,726,2,0.25,0.0
|
| 214 |
+
700.000 eller derover,4.0,1.0,58,,0.0,728,2,0.25,
|
| 215 |
+
600.000 - 699.999 kr.,4.0,0.0,47,1.0,0.0,730,2,0.75,0.0
|
| 216 |
+
100.000 - 199.999 kr.,3.0,1.0,25,,0.0,732,2,0.75,
|
| 217 |
+
600.000 - 699.999 kr.,1.0,0.0,38,0.0,0.0,744,2,0.75,0.0
|
| 218 |
+
700.000 eller derover,4.0,0.0,53,1.0,0.0,747,2,0.75,0.0
|
| 219 |
+
200.000 - 299.999 kr.,4.0,0.0,34,1.0,0.0,751,2,1.0,0.0
|
| 220 |
+
100.000 - 199.999 kr.,2.0,1.0,29,,0.0,761,2,0.5,
|
| 221 |
+
700.000 eller derover,4.0,1.0,60,1.0,0.0,763,2,0.5,0.0
|
| 222 |
+
600.000 - 699.999 kr.,4.0,1.0,60,,0.0,772,2,0.75,
|
| 223 |
+
300.000 - 399.999 kr.,2.0,1.0,64,,0.0,775,2,0.75,
|
| 224 |
+
Onsker ikke at oplyse,4.0,1.0,58,0.0,0.0,781,2,0.5,0.0
|
| 225 |
+
200.000 - 299.999 kr.,2.0,1.0,36,,0.0,784,2,0.25,
|
| 226 |
+
700.000 eller derover,4.0,0.0,43,1.0,0.0,806,2,0.75,0.0
|
| 227 |
+
100.000 - 199.999 kr.,4.0,1.0,33,,0.0,807,2,1.0,
|
| 228 |
+
200.000 - 299.999 kr.,2.0,1.0,43,1.0,0.0,809,2,1.0,0.0
|
| 229 |
+
600.000 - 699.999 kr.,4.0,1.0,50,,0.0,810,2,0.5,
|
| 230 |
+
Onsker ikke at oplyse,4.0,0.0,62,,0.0,811,2,0.75,
|
| 231 |
+
300.000 - 399.999 kr.,4.0,1.0,50,1.0,0.0,812,2,0.5,0.0
|
| 232 |
+
700.000 eller derover,2.0,1.0,56,0.0,0.0,813,2,0.25,0.0
|
| 233 |
+
700.000 eller derover,4.0,1.0,43,0.0,0.0,821,2,0.75,0.0
|
| 234 |
+
Onsker ikke at oplyse,3.0,1.0,40,,0.0,822,2,1.0,
|
| 235 |
+
Onsker ikke at oplyse,1.0,1.0,60,,0.0,823,2,0.5,
|
| 236 |
+
500.000 - 599.999 kr.,4.0,1.0,56,1.0,0.0,824,2,0.5,0.0
|
| 237 |
+
700.000 eller derover,4.0,1.0,39,,0.0,825,2,0.75,
|
| 238 |
+
300.000 - 399.999 kr.,4.0,1.0,41,,0.0,834,2,0.25,
|
| 239 |
+
700.000 eller derover,5.0,1.0,55,,0.0,837,2,0.5,
|
| 240 |
+
300.000 - 399.999 kr.,5.0,1.0,33,,0.0,839,2,0.75,
|
| 241 |
+
700.000 eller derover,2.0,0.0,41,0.0,0.0,841,2,0.5,0.0
|
| 242 |
+
700.000 eller derover,4.0,1.0,55,,0.0,844,2,0.25,
|
| 243 |
+
700.000 eller derover,2.0,1.0,46,1.0,0.0,850,2,0.75,0.0
|
| 244 |
+
300.000 - 399.999 kr.,4.0,0.0,43,1.0,0.0,853,2,0.75,0.0
|
| 245 |
+
600.000 - 699.999 kr.,4.0,0.0,45,,0.0,854,2,0.75,
|
| 246 |
+
400.000 - 499.999 kr.,4.0,1.0,73,,0.0,859,2,0.5,
|
| 247 |
+
300.000 - 399.999 kr.,1.0,0.0,57,1.0,0.0,863,2,1.0,0.0
|
| 248 |
+
700.000 eller derover,2.0,0.0,48,,0.0,865,2,0.75,
|
| 249 |
+
200.000 - 299.999 kr.,3.0,1.0,58,1.0,0.0,866,2,0.5,0.0
|
| 250 |
+
100.000 - 199.999 kr.,5.0,1.0,55,,0.0,878,2,0.75,
|
| 251 |
+
600.000 - 699.999 kr.,2.0,1.0,54,0.0,0.0,881,2,0.5,0.0
|
| 252 |
+
400.000 - 499.999 kr.,2.0,1.0,58,,0.0,884,2,0.5,
|
| 253 |
+
Indtil 99.999 kr.,3.0,1.0,19,1.0,0.0,886,2,0.5,0.0
|
| 254 |
+
300.000 - 399.999 kr.,5.0,1.0,62,0.0,0.0,889,2,0.75,0.0
|
| 255 |
+
200.000 - 299.999 kr.,4.0,0.0,40,,0.0,893,2,0.5,
|
| 256 |
+
600.000 - 699.999 kr.,2.0,1.0,59,1.0,0.0,895,2,0.75,0.0
|
| 257 |
+
700.000 eller derover,5.0,1.0,57,0.0,0.0,897,2,0.5,0.0
|
| 258 |
+
Onsker ikke at oplyse,2.0,1.0,29,0.0,0.0,899,2,0.5,0.0
|
| 259 |
+
400.000 - 499.999 kr.,1.0,1.0,44,1.0,0.0,900,2,0.75,0.0
|
| 260 |
+
700.000 eller derover,5.0,1.0,37,0.0,0.0,902,2,0.5,0.0
|
| 261 |
+
200.000 - 299.999 kr.,2.0,0.0,39,,0.0,907,2,0.75,
|
| 262 |
+
100.000 - 199.999 kr.,3.0,0.0,38,0.0,0.0,908,2,0.5,0.0
|
| 263 |
+
500.000 - 599.999 kr.,4.0,1.0,48,1.0,0.0,911,2,0.75,0.0
|
| 264 |
+
500.000 - 599.999 kr.,4.0,0.0,60,0.0,0.0,915,2,0.5,0.0
|
| 265 |
+
Onsker ikke at oplyse,5.0,1.0,48,0.0,0.0,920,2,0.5,0.0
|
| 266 |
+
400.000 - 499.999 kr.,4.0,1.0,33,1.0,0.0,923,2,0.5,0.0
|
| 267 |
+
400.000 - 499.999 kr.,5.0,0.0,33,1.0,0.0,924,2,0.5,0.0
|
| 268 |
+
600.000 - 699.999 kr.,2.0,0.0,42,1.0,0.0,926,2,0.75,0.0
|
| 269 |
+
700.000 eller derover,4.0,1.0,55,1.0,0.0,931,2,0.25,0.0
|
| 270 |
+
400.000 - 499.999 kr.,5.0,1.0,70,0.0,0.0,936,2,1.0,0.0
|
| 271 |
+
700.000 eller derover,5.0,0.0,40,,0.0,937,2,0.75,
|
| 272 |
+
400.000 - 499.999 kr.,5.0,1.0,58,0.0,0.0,950,2,0.5,0.0
|
| 273 |
+
700.000 eller derover,4.0,1.0,43,0.0,0.0,958,2,0.5,0.0
|
| 274 |
+
600.000 - 699.999 kr.,5.0,0.0,29,1.0,0.0,962,2,0.25,0.0
|
| 275 |
+
600.000 - 699.999 kr.,4.0,1.0,49,0.0,0.0,964,2,0.25,0.0
|
| 276 |
+
700.000 eller derover,5.0,1.0,41,0.0,0.0,966,2,1.0,0.0
|
| 277 |
+
200.000 - 299.999 kr.,4.0,0.0,47,,0.0,969,2,0.5,
|
| 278 |
+
700.000 eller derover,2.0,1.0,50,0.0,0.0,973,2,0.25,0.0
|
| 279 |
+
Indtil 99.999 kr.,3.0,0.0,25,1.0,0.0,975,2,0.5,0.0
|
| 280 |
+
500.000 - 599.999 kr.,5.0,1.0,59,,0.0,980,2,0.25,
|
| 281 |
+
700.000 eller derover,2.0,1.0,46,0.0,0.0,981,2,0.5,0.0
|
| 282 |
+
700.000 eller derover,4.0,0.0,27,0.0,0.0,982,2,0.75,0.0
|
| 283 |
+
300.000 - 399.999 kr.,5.0,1.0,59,1.0,0.0,984,2,0.5,0.0
|
| 284 |
+
700.000 eller derover,1.0,1.0,53,1.0,0.0,987,2,0.75,0.0
|
| 285 |
+
700.000 eller derover,5.0,0.0,37,1.0,0.0,988,2,0.75,0.0
|
| 286 |
+
Onsker ikke at oplyse,5.0,0.0,57,,0.0,992,2,1.0,
|
| 287 |
+
700.000 eller derover,5.0,0.0,38,1.0,0.0,993,2,0.75,0.0
|
| 288 |
+
700.000 eller derover,5.0,1.0,57,,0.0,994,2,0.5,
|
| 289 |
+
Onsker ikke at oplyse,5.0,1.0,56,1.0,0.0,997,2,0.5,0.0
|
| 290 |
+
700.000 eller derover,4.0,0.0,59,1.0,0.0,999,2,1.0,0.0
|
| 291 |
+
600.000 - 699.999 kr.,4.0,1.0,57,1.0,0.0,1001,2,0.5,0.0
|
| 292 |
+
700.000 eller derover,5.0,0.0,53,1.0,0.0,1002,2,0.5,0.0
|
| 293 |
+
700.000 eller derover,4.0,1.0,40,0.0,0.0,1004,2,0.5,0.0
|
| 294 |
+
700.000 eller derover,4.0,1.0,57,0.0,0.0,1006,2,1.0,0.0
|
| 295 |
+
600.000 - 699.999 kr.,5.0,1.0,52,1.0,0.0,1008,2,0.5,0.0
|
| 296 |
+
500.000 - 599.999 kr.,4.0,1.0,53,,0.0,1011,2,0.25,
|
| 297 |
+
500.000 - 599.999 kr.,5.0,0.0,63,,0.0,1012,2,0.25,
|
| 298 |
+
400.000 - 499.999 kr.,4.0,0.0,46,1.0,0.0,1013,2,1.0,0.0
|
| 299 |
+
400.000 - 499.999 kr.,2.0,0.0,43,1.0,0.0,1016,2,0.75,0.0
|
| 300 |
+
100.000 - 199.999 kr.,4.0,1.0,33,,0.0,1017,2,0.75,
|
| 301 |
+
600.000 - 699.999 kr.,5.0,0.0,67,,0.0,1020,2,0.75,
|
| 302 |
+
500.000 - 599.999 kr.,1.0,1.0,57,1.0,0.0,1027,2,0.75,0.0
|
| 303 |
+
700.000 eller derover,5.0,1.0,38,,0.0,1029,2,0.5,
|
| 304 |
+
700.000 eller derover,4.0,1.0,41,0.0,0.0,1031,2,0.5,0.0
|
| 305 |
+
700.000 eller derover,5.0,1.0,46,,0.0,1035,2,0.0,
|
| 306 |
+
200.000 - 299.999 kr.,2.0,0.0,46,,0.0,1036,2,0.5,
|
| 307 |
+
500.000 - 599.999 kr.,2.0,1.0,51,1.0,0.0,1039,2,0.5,0.0
|
| 308 |
+
700.000 eller derover,3.0,1.0,45,0.0,0.0,1041,2,0.75,0.0
|
| 309 |
+
700.000 eller derover,5.0,1.0,42,1.0,0.0,1043,2,0.75,0.0
|
| 310 |
+
500.000 - 599.999 kr.,2.0,0.0,49,0.0,0.0,1044,2,0.25,0.0
|
| 311 |
+
700.000 eller derover,4.0,1.0,58,,0.0,1047,2,0.5,
|
| 312 |
+
400.000 - 499.999 kr.,2.0,0.0,58,0.0,0.0,1048,2,0.25,0.0
|
| 313 |
+
100.000 - 199.999 kr.,3.0,1.0,24,0.0,0.0,1059,2,0.75,0.0
|
| 314 |
+
700.000 eller derover,5.0,1.0,33,,0.0,1061,2,1.0,
|
| 315 |
+
Onsker ikke at oplyse,5.0,0.0,43,0.0,0.0,1066,2,0.5,0.0
|
| 316 |
+
Onsker ikke at oplyse,2.0,1.0,53,1.0,0.0,1070,2,0.75,0.0
|
| 317 |
+
600.000 - 699.999 kr.,2.0,0.0,52,,0.0,1076,2,0.5,
|
| 318 |
+
700.000 eller derover,5.0,1.0,32,0.0,0.0,1078,2,0.5,0.0
|
| 319 |
+
400.000 - 499.999 kr.,2.0,0.0,59,1.0,0.0,1079,2,0.75,0.0
|
| 320 |
+
500.000 - 599.999 kr.,4.0,0.0,56,1.0,0.0,1085,2,1.0,0.0
|
| 321 |
+
600.000 - 699.999 kr.,3.0,1.0,55,,0.0,1086,2,0.5,
|
| 322 |
+
600.000 - 699.999 kr.,3.0,1.0,55,,0.0,1087,2,0.25,
|
| 323 |
+
500.000 - 599.999 kr.,5.0,0.0,30,,0.0,1095,2,0.5,
|
| 324 |
+
300.000 - 399.999 kr.,2.0,1.0,42,,0.0,1102,2,0.75,
|
| 325 |
+
700.000 eller derover,4.0,0.0,47,0.0,0.0,1103,2,0.5,0.0
|
| 326 |
+
600.000 - 699.999 kr.,4.0,1.0,41,1.0,0.0,1109,2,0.5,0.0
|
| 327 |
+
500.000 - 599.999 kr.,4.0,1.0,33,1.0,0.0,1114,2,1.0,0.0
|
| 328 |
+
600.000 - 699.999 kr.,1.0,0.0,6,,0.0,1115,2,0.5,
|
| 329 |
+
300.000 - 399.999 kr.,2.0,0.0,60,1.0,0.0,1120,2,0.75,0.0
|
| 330 |
+
300.000 - 399.999 kr.,4.0,0.0,49,1.0,0.0,1121,2,0.5,0.0
|
| 331 |
+
500.000 - 599.999 kr.,2.0,1.0,61,0.0,0.0,1122,2,0.75,0.0
|
| 332 |
+
Onsker ikke at oplyse,5.0,1.0,54,0.0,0.0,1125,2,0.5,0.0
|
| 333 |
+
600.000 - 699.999 kr.,2.0,1.0,51,,0.0,1129,2,0.5,
|
| 334 |
+
500.000 - 599.999 kr.,2.0,1.0,58,0.0,0.0,1130,2,0.5,0.0
|
| 335 |
+
700.000 eller derover,4.0,0.0,53,1.0,0.0,1131,2,0.75,0.0
|
| 336 |
+
500.000 - 599.999 kr.,4.0,0.0,63,,0.0,1132,2,0.5,
|
| 337 |
+
300.000 - 399.999 kr.,4.0,1.0,54,1.0,0.0,1135,2,0.5,0.0
|
| 338 |
+
700.000 eller derover,5.0,1.0,64,1.0,0.0,1137,2,0.75,0.0
|
| 339 |
+
600.000 - 699.999 kr.,4.0,1.0,46,0.0,0.0,1139,2,0.5,0.0
|
| 340 |
+
700.000 eller derover,2.0,0.0,52,1.0,0.0,1141,2,0.75,0.0
|
| 341 |
+
500.000 - 599.999 kr.,4.0,1.0,52,,0.0,1142,2,1.0,
|
| 342 |
+
Onsker ikke at oplyse,3.0,0.0,51,,0.0,1145,2,1.0,
|
| 343 |
+
500.000 - 599.999 kr.,4.0,1.0,59,,0.0,1150,2,0.5,
|
| 344 |
+
200.000 - 299.999 kr.,1.0,1.0,65,1.0,0.0,1162,2,0.75,0.0
|
| 345 |
+
700.000 eller derover,4.0,0.0,40,,0.0,1164,2,0.5,
|
| 346 |
+
700.000 eller derover,2.0,1.0,53,,0.0,1171,2,0.25,
|
| 347 |
+
400.000 - 499.999 kr.,5.0,1.0,44,0.0,0.0,1173,2,0.75,0.0
|
| 348 |
+
100.000 - 199.999 kr.,2.0,1.0,78,1.0,0.0,1174,2,0.5,0.0
|
| 349 |
+
700.000 eller derover,5.0,1.0,29,0.0,0.0,1182,2,0.5,0.0
|
| 350 |
+
700.000 eller derover,2.0,1.0,32,0.0,0.0,1187,2,0.25,0.0
|
| 351 |
+
600.000 - 699.999 kr.,4.0,1.0,26,1.0,0.0,1195,2,0.0,0.0
|
| 352 |
+
100.000 - 199.999 kr.,1.0,1.0,30,1.0,0.0,1197,2,0.75,0.0
|
| 353 |
+
200.000 - 299.999 kr.,2.0,1.0,59,0.0,0.0,1199,2,0.75,0.0
|
| 354 |
+
500.000 - 599.999 kr.,1.0,1.0,58,1.0,0.0,1200,2,0.75,0.0
|
| 355 |
+
500.000 - 599.999 kr.,2.0,0.0,49,,0.0,1203,2,0.5,
|
| 356 |
+
300.000 - 399.999 kr.,3.0,1.0,42,1.0,0.0,1206,2,1.0,0.0
|
| 357 |
+
100.000 - 199.999 kr.,4.0,0.0,63,,0.0,1211,2,0.25,
|
| 358 |
+
300.000 - 399.999 kr.,2.0,0.0,45,,0.0,1213,2,1.0,
|
| 359 |
+
500.000 - 599.999 kr.,4.0,0.0,41,,0.0,1219,2,0.5,
|
| 360 |
+
500.000 - 599.999 kr.,2.0,1.0,35,1.0,0.0,1224,2,0.75,0.0
|
| 361 |
+
Onsker ikke at oplyse,4.0,1.0,43,1.0,0.0,1230,2,0.75,0.0
|
| 362 |
+
300.000 - 399.999 kr.,2.0,1.0,35,0.0,0.0,1235,2,0.25,0.0
|
| 363 |
+
300.000 - 399.999 kr.,5.0,0.0,44,1.0,0.0,1237,2,1.0,0.0
|
| 364 |
+
300.000 - 399.999 kr.,4.0,0.0,52,,0.0,1245,2,0.75,
|
| 365 |
+
400.000 - 499.999 kr.,3.0,1.0,58,,0.0,1247,2,0.5,
|
| 366 |
+
100.000 - 199.999 kr.,4.0,0.0,46,,0.0,1248,2,0.5,
|
| 367 |
+
500.000 - 599.999 kr.,2.0,1.0,40,1.0,0.0,1250,2,1.0,0.0
|
| 368 |
+
100.000 - 199.999 kr.,2.0,1.0,43,,0.0,1252,2,1.0,
|
| 369 |
+
100.000 - 199.999 kr.,4.0,0.0,54,1.0,0.0,1259,2,0.75,0.0
|
| 370 |
+
500.000 - 599.999 kr.,4.0,1.0,60,,0.0,1261,2,0.75,
|
| 371 |
+
700.000 eller derover,4.0,1.0,52,0.0,0.0,1263,2,0.75,0.0
|
| 372 |
+
600.000 - 699.999 kr.,1.0,1.0,59,1.0,0.0,1264,2,0.5,0.0
|
| 373 |
+
200.000 - 299.999 kr.,4.0,1.0,62,,0.0,1267,2,1.0,
|
| 374 |
+
Onsker ikke at oplyse,3.0,1.0,19,,0.0,1275,2,0.0,
|
| 375 |
+
600.000 - 699.999 kr.,3.0,0.0,51,1.0,0.0,1277,2,0.75,0.0
|
| 376 |
+
100.000 - 199.999 kr.,4.0,0.0,53,1.0,0.0,1281,2,0.75,0.0
|
| 377 |
+
300.000 - 399.999 kr.,5.0,0.0,28,,0.0,1282,2,0.5,
|
| 378 |
+
600.000 - 699.999 kr.,2.0,0.0,50,1.0,0.0,1286,2,0.5,0.0
|
| 379 |
+
300.000 - 399.999 kr.,1.0,0.0,58,1.0,0.0,1294,2,1.0,0.0
|
| 380 |
+
600.000 - 699.999 kr.,4.0,1.0,56,1.0,0.0,1295,2,0.5,0.0
|
| 381 |
+
600.000 - 699.999 kr.,5.0,1.0,44,1.0,0.0,1296,2,0.5,0.0
|
| 382 |
+
Onsker ikke at oplyse,4.0,0.0,55,,0.0,1297,2,0.25,
|
| 383 |
+
300.000 - 399.999 kr.,3.0,0.0,23,,0.0,1304,2,0.75,
|
| 384 |
+
300.000 - 399.999 kr.,4.0,1.0,62,1.0,0.0,1307,2,0.75,0.0
|
| 385 |
+
700.000 eller derover,4.0,0.0,48,,0.0,1308,2,0.25,
|
| 386 |
+
700.000 eller derover,4.0,1.0,30,,0.0,1313,2,0.5,
|
| 387 |
+
600.000 - 699.999 kr.,5.0,0.0,28,0.0,0.0,1314,2,0.75,0.0
|
| 388 |
+
700.000 eller derover,5.0,1.0,53,1.0,0.0,1315,2,0.75,0.0
|
| 389 |
+
100.000 - 199.999 kr.,1.0,1.0,65,0.0,0.0,1320,2,0.75,0.0
|
| 390 |
+
200.000 - 299.999 kr.,2.0,1.0,65,,0.0,1323,2,0.5,
|
| 391 |
+
500.000 - 599.999 kr.,2.0,0.0,45,0.0,0.0,1324,2,0.5,0.0
|
| 392 |
+
200.000 - 299.999 kr.,4.0,0.0,67,1.0,0.0,1325,2,0.75,0.0
|
| 393 |
+
500.000 - 599.999 kr.,4.0,0.0,53,,0.0,1330,2,0.5,
|
| 394 |
+
Onsker ikke at oplyse,2.0,0.0,55,,0.0,1339,2,0.75,
|
| 395 |
+
400.000 - 499.999 kr.,2.0,1.0,37,0.0,0.0,1344,2,0.5,0.0
|
| 396 |
+
700.000 eller derover,5.0,1.0,50,0.0,0.0,1346,2,0.25,0.0
|
| 397 |
+
400.000 - 499.999 kr.,4.0,1.0,85,0.0,0.0,1347,2,0.5,0.0
|
| 398 |
+
400.000 - 499.999 kr.,4.0,0.0,58,0.0,0.0,1348,2,0.5,0.0
|
| 399 |
+
500.000 - 599.999 kr.,4.0,0.0,56,0.0,0.0,1353,2,0.5,0.0
|
| 400 |
+
400.000 - 499.999 kr.,2.0,1.0,51,1.0,0.0,1355,2,0.75,0.0
|
| 401 |
+
300.000 - 399.999 kr.,2.0,0.0,64,,0.0,1358,2,0.5,
|
| 402 |
+
600.000 - 699.999 kr.,2.0,1.0,52,,0.0,1361,2,0.75,
|
| 403 |
+
700.000 eller derover,5.0,1.0,49,,0.0,1362,2,0.75,
|
| 404 |
+
700.000 eller derover,4.0,1.0,36,0.0,0.0,1367,2,1.0,0.0
|
| 405 |
+
700.000 eller derover,3.0,1.0,55,0.0,0.0,1369,2,0.5,0.0
|
| 406 |
+
300.000 - 399.999 kr.,5.0,1.0,61,0.0,0.0,1370,2,0.5,0.0
|
| 407 |
+
700.000 eller derover,5.0,0.0,42,1.0,0.0,1371,2,0.5,0.0
|
| 408 |
+
300.000 - 399.999 kr.,5.0,0.0,38,1.0,0.0,1373,2,0.25,0.0
|
| 409 |
+
400.000 - 499.999 kr.,4.0,1.0,63,1.0,0.0,1375,2,0.75,0.0
|
| 410 |
+
400.000 - 499.999 kr.,5.0,1.0,66,,0.0,1380,2,1.0,
|
| 411 |
+
Indtil 99.999 kr.,3.0,0.0,21,1.0,0.0,1384,2,0.75,0.0
|
| 412 |
+
100.000 - 199.999 kr.,3.0,0.0,21,1.0,0.0,1385,2,0.0,0.0
|
| 413 |
+
200.000 - 299.999 kr.,5.0,1.0,28,,0.0,1390,2,0.5,
|
| 414 |
+
600.000 - 699.999 kr.,4.0,0.0,53,1.0,0.0,1392,2,0.5,0.0
|
| 415 |
+
300.000 - 399.999 kr.,4.0,1.0,26,0.0,0.0,1393,2,0.25,0.0
|
| 416 |
+
200.000 - 299.999 kr.,2.0,1.0,64,0.0,0.0,1396,2,0.5,0.0
|
| 417 |
+
400.000 - 499.999 kr.,1.0,1.0,57,1.0,0.0,1399,2,0.5,0.0
|
| 418 |
+
700.000 eller derover,5.0,1.0,33,0.0,0.0,1402,2,1.0,0.0
|
| 419 |
+
600.000 - 699.999 kr.,4.0,0.0,42,1.0,0.0,1403,2,0.5,0.0
|
| 420 |
+
300.000 - 399.999 kr.,4.0,1.0,48,0.0,0.0,1406,2,0.5,0.0
|
| 421 |
+
Onsker ikke at oplyse,3.0,0.0,19,1.0,0.0,1409,2,0.5,0.0
|
| 422 |
+
600.000 - 699.999 kr.,4.0,1.0,42,1.0,0.0,1412,2,0.75,0.0
|
| 423 |
+
600.000 - 699.999 kr.,4.0,0.0,53,1.0,0.0,1413,2,0.75,0.0
|
| 424 |
+
700.000 eller derover,4.0,1.0,58,1.0,0.0,1416,2,0.75,0.0
|
| 425 |
+
100.000 - 199.999 kr.,4.0,1.0,43,1.0,0.0,1417,2,1.0,0.0
|
| 426 |
+
Onsker ikke at oplyse,2.0,0.0,55,0.0,0.0,1422,2,0.75,0.0
|
| 427 |
+
600.000 - 699.999 kr.,3.0,0.0,46,1.0,0.0,1426,2,0.75,0.0
|
| 428 |
+
700.000 eller derover,4.0,1.0,57,1.0,0.0,1431,2,0.75,0.0
|
| 429 |
+
500.000 - 599.999 kr.,4.0,0.0,57,1.0,0.0,1433,2,0.5,0.0
|
| 430 |
+
300.000 - 399.999 kr.,4.0,1.0,97,0.0,0.0,1437,2,0.25,0.0
|
| 431 |
+
700.000 eller derover,2.0,1.0,49,,0.0,1439,2,0.5,
|
| 432 |
+
Indtil 99.999 kr.,5.0,1.0,25,0.0,0.0,1440,2,1.0,0.0
|
| 433 |
+
200.000 - 299.999 kr.,4.0,1.0,64,,0.0,1441,2,0.5,
|
| 434 |
+
600.000 - 699.999 kr.,5.0,0.0,54,,0.0,1442,2,0.5,
|
| 435 |
+
Onsker ikke at oplyse,5.0,1.0,33,0.0,0.0,1444,2,0.25,0.0
|
| 436 |
+
700.000 eller derover,5.0,1.0,85,0.0,0.0,1446,2,0.5,0.0
|
| 437 |
+
500.000 - 599.999 kr.,4.0,0.0,43,1.0,0.0,1447,2,0.25,0.0
|
| 438 |
+
700.000 eller derover,5.0,1.0,69,1.0,0.0,1448,2,1.0,0.0
|
| 439 |
+
100.000 - 199.999 kr.,3.0,1.0,26,,0.0,1459,2,0.5,
|
| 440 |
+
700.000 eller derover,5.0,1.0,56,0.0,0.0,1464,2,0.5,0.0
|
| 441 |
+
200.000 - 299.999 kr.,3.0,1.0,56,0.0,0.0,1473,2,0.5,0.0
|
| 442 |
+
500.000 - 599.999 kr.,4.0,1.0,46,,0.0,1477,2,1.0,
|
| 443 |
+
100.000 - 199.999 kr.,3.0,0.0,25,1.0,0.0,1482,2,0.75,0.0
|
| 444 |
+
600.000 - 699.999 kr.,5.0,1.0,55,0.0,0.0,1489,2,0.5,0.0
|
| 445 |
+
600.000 - 699.999 kr.,5.0,1.0,59,1.0,0.0,1493,2,0.5,0.0
|
| 446 |
+
400.000 - 499.999 kr.,1.0,1.0,56,1.0,0.0,1494,2,0.75,0.0
|
| 447 |
+
700.000 eller derover,1.0,1.0,47,,0.0,1497,2,0.75,
|
| 448 |
+
600.000 - 699.999 kr.,4.0,0.0,73,1.0,0.0,1499,2,0.5,0.0
|
| 449 |
+
300.000 - 399.999 kr.,4.0,1.0,47,1.0,0.0,1501,2,1.0,0.0
|
| 450 |
+
700.000 eller derover,2.0,0.0,55,,0.0,1504,2,0.5,
|
| 451 |
+
600.000 - 699.999 kr.,4.0,1.0,57,1.0,0.0,1507,2,0.5,0.0
|
| 452 |
+
700.000 eller derover,4.0,1.0,53,1.0,0.0,1508,2,0.75,0.0
|
| 453 |
+
300.000 - 399.999 kr.,2.0,0.0,52,1.0,0.0,1511,2,0.5,0.0
|
| 454 |
+
700.000 eller derover,4.0,1.0,44,0.0,0.0,1512,2,0.5,0.0
|
| 455 |
+
600.000 - 699.999 kr.,4.0,1.0,46,1.0,0.0,1516,2,0.5,0.0
|
| 456 |
+
700.000 eller derover,5.0,0.0,44,,0.0,1517,2,0.5,
|
| 457 |
+
700.000 eller derover,5.0,0.0,40,0.0,0.0,1518,2,0.5,0.0
|
| 458 |
+
700.000 eller derover,4.0,1.0,41,1.0,0.0,1521,2,0.5,0.0
|
| 459 |
+
300.000 - 399.999 kr.,4.0,0.0,49,1.0,0.0,1523,2,0.25,0.0
|
| 460 |
+
500.000 - 599.999 kr.,1.0,1.0,58,1.0,0.0,1528,2,0.5,0.0
|
| 461 |
+
400.000 - 499.999 kr.,3.0,1.0,50,1.0,0.0,1530,2,0.75,0.0
|
| 462 |
+
500.000 - 599.999 kr.,2.0,1.0,52,0.0,0.0,1532,2,1.0,0.0
|
| 463 |
+
600.000 - 699.999 kr.,4.0,0.0,37,,0.0,1536,2,0.25,
|
| 464 |
+
200.000 - 299.999 kr.,1.0,0.0,42,,0.0,1538,2,0.75,
|
| 465 |
+
100.000 - 199.999 kr.,2.0,1.0,44,1.0,0.0,1542,2,1.0,0.0
|
| 466 |
+
100.000 - 199.999 kr.,2.0,1.0,69,1.0,0.0,1544,2,0.25,0.0
|
| 467 |
+
700.000 eller derover,2.0,1.0,65,1.0,0.0,1545,2,0.5,0.0
|
| 468 |
+
600.000 - 699.999 kr.,4.0,1.0,35,,0.0,1548,2,0.5,
|
| 469 |
+
Indtil 99.999 kr.,5.0,1.0,25,,0.0,1549,2,0.75,
|
| 470 |
+
Onsker ikke at oplyse,2.0,1.0,45,,0.0,1551,2,0.5,
|
| 471 |
+
200.000 - 299.999 kr.,4.0,0.0,59,1.0,0.0,1554,2,1.0,0.0
|
| 472 |
+
400.000 - 499.999 kr.,2.0,1.0,42,0.0,0.0,1561,2,0.5,0.0
|
| 473 |
+
700.000 eller derover,5.0,1.0,52,1.0,0.0,1562,2,0.5,0.0
|
| 474 |
+
700.000 eller derover,3.0,0.0,19,,0.0,1565,2,0.75,
|
| 475 |
+
700.000 eller derover,5.0,1.0,43,,0.0,1569,2,0.5,
|
| 476 |
+
300.000 - 399.999 kr.,2.0,1.0,62,0.0,0.0,1578,2,0.5,0.0
|
| 477 |
+
600.000 - 699.999 kr.,2.0,0.0,44,1.0,0.0,1587,2,0.75,0.0
|
| 478 |
+
600.000 - 699.999 kr.,2.0,1.0,31,0.0,0.0,1592,2,0.75,0.0
|
| 479 |
+
700.000 eller derover,4.0,1.0,37,0.0,0.0,1595,2,0.75,0.0
|
| 480 |
+
700.000 eller derover,5.0,0.0,47,1.0,0.0,1598,2,0.75,0.0
|
| 481 |
+
300.000 - 399.999 kr.,4.0,1.0,52,1.0,0.0,1599,2,0.75,0.0
|
| 482 |
+
Onsker ikke at oplyse,4.0,0.0,65,,0.0,1611,2,0.5,
|
| 483 |
+
700.000 eller derover,4.0,1.0,47,1.0,0.0,1612,2,0.75,0.0
|
| 484 |
+
400.000 - 499.999 kr.,2.0,1.0,49,1.0,0.0,1614,2,0.5,0.0
|
| 485 |
+
200.000 - 299.999 kr.,1.0,1.0,48,1.0,0.0,1616,2,0.75,0.0
|
| 486 |
+
200.000 - 299.999 kr.,4.0,0.0,59,,0.0,1617,2,0.5,
|
| 487 |
+
600.000 - 699.999 kr.,4.0,0.0,38,,0.0,1625,2,0.25,
|
| 488 |
+
700.000 eller derover,5.0,1.0,34,,0.0,1634,2,0.5,
|
| 489 |
+
400.000 - 499.999 kr.,4.0,0.0,55,,0.0,1636,2,0.5,
|
| 490 |
+
100.000 - 199.999 kr.,4.0,0.0,65,,0.0,1637,2,0.5,
|
| 491 |
+
300.000 - 399.999 kr.,4.0,1.0,51,0.0,0.0,1641,2,0.25,0.0
|
| 492 |
+
600.000 - 699.999 kr.,2.0,0.0,46,,0.0,1648,2,0.5,
|
| 493 |
+
300.000 - 399.999 kr.,4.0,0.0,64,1.0,0.0,1651,2,0.5,0.0
|
| 494 |
+
500.000 - 599.999 kr.,5.0,1.0,28,0.0,0.0,1654,2,0.75,0.0
|
| 495 |
+
300.000 - 399.999 kr.,4.0,1.0,31,,0.0,1664,2,0.75,
|
| 496 |
+
200.000 - 299.999 kr.,4.0,0.0,56,1.0,0.0,1672,2,0.75,0.0
|
| 497 |
+
200.000 - 299.999 kr.,3.0,1.0,29,,0.0,1673,2,0.75,
|
| 498 |
+
700.000 eller derover,4.0,1.0,63,1.0,0.0,1674,2,1.0,0.0
|
| 499 |
+
500.000 - 599.999 kr.,4.0,0.0,54,1.0,0.0,1675,2,0.75,0.0
|
| 500 |
+
700.000 eller derover,5.0,0.0,49,1.0,0.0,1676,2,0.5,0.0
|
| 501 |
+
700.000 eller derover,5.0,1.0,50,1.0,0.0,1677,2,0.5,0.0
|
| 502 |
+
100.000 - 199.999 kr.,2.0,1.0,82,,0.0,1678,2,0.5,
|
| 503 |
+
300.000 - 399.999 kr.,3.0,0.0,52,0.0,0.0,1681,2,0.5,0.0
|
| 504 |
+
300.000 - 399.999 kr.,2.0,1.0,53,1.0,0.0,1692,2,0.75,0.0
|
| 505 |
+
700.000 eller derover,5.0,1.0,55,0.0,0.0,1696,2,0.75,0.0
|
| 506 |
+
700.000 eller derover,5.0,1.0,54,1.0,0.0,1698,2,0.75,0.0
|
| 507 |
+
700.000 eller derover,4.0,0.0,21,0.0,0.0,1704,2,0.5,0.0
|
| 508 |
+
400.000 - 499.999 kr.,5.0,1.0,45,,0.0,1705,2,0.25,
|
| 509 |
+
700.000 eller derover,5.0,1.0,47,,0.0,1709,2,0.75,
|
| 510 |
+
200.000 - 299.999 kr.,1.0,1.0,65,0.0,0.0,1714,2,0.25,0.0
|
| 511 |
+
700.000 eller derover,4.0,1.0,36,0.0,0.0,1716,2,0.75,0.0
|
| 512 |
+
600.000 - 699.999 kr.,2.0,1.0,49,0.0,0.0,1717,2,0.5,0.0
|
| 513 |
+
500.000 - 599.999 kr.,3.0,1.0,37,0.0,0.0,1718,2,0.5,0.0
|
| 514 |
+
500.000 - 599.999 kr.,2.0,0.0,49,0.0,0.0,1720,2,0.5,0.0
|
| 515 |
+
500.000 - 599.999 kr.,3.0,0.0,40,0.0,0.0,1721,2,0.25,0.0
|
| 516 |
+
300.000 - 399.999 kr.,4.0,1.0,52,,0.0,1722,2,0.25,
|
| 517 |
+
600.000 - 699.999 kr.,4.0,1.0,57,0.0,0.0,1724,2,0.5,0.0
|
| 518 |
+
400.000 - 499.999 kr.,4.0,1.0,57,0.0,0.0,1728,2,0.75,0.0
|
| 519 |
+
100.000 - 199.999 kr.,5.0,1.0,25,,0.0,1729,2,0.5,
|
| 520 |
+
300.000 - 399.999 kr.,5.0,0.0,58,,0.0,1735,2,1.0,
|
| 521 |
+
400.000 - 499.999 kr.,5.0,1.0,47,1.0,0.0,1737,2,0.5,0.0
|
| 522 |
+
700.000 eller derover,5.0,1.0,50,0.0,0.0,1741,2,0.5,0.0
|
| 523 |
+
600.000 - 699.999 kr.,2.0,1.0,37,1.0,0.0,1753,2,0.5,0.0
|
| 524 |
+
400.000 - 499.999 kr.,4.0,0.0,34,0.0,0.0,1754,2,0.25,0.0
|
| 525 |
+
200.000 - 299.999 kr.,2.0,1.0,48,1.0,0.0,1758,2,0.5,0.0
|
| 526 |
+
700.000 eller derover,4.0,1.0,64,,0.0,1760,2,0.75,
|
| 527 |
+
300.000 - 399.999 kr.,4.0,1.0,65,,0.0,1764,2,0.25,
|
| 528 |
+
500.000 - 599.999 kr.,4.0,1.0,58,1.0,0.0,1765,2,0.5,0.0
|
| 529 |
+
500.000 - 599.999 kr.,2.0,1.0,58,,0.0,1768,2,0.5,
|
| 530 |
+
300.000 - 399.999 kr.,4.0,0.0,75,0.0,0.0,1770,2,0.75,0.0
|
| 531 |
+
400.000 - 499.999 kr.,2.0,1.0,40,,0.0,1777,2,0.5,
|
| 532 |
+
600.000 - 699.999 kr.,5.0,1.0,59,,0.0,1779,2,0.5,
|
| 533 |
+
200.000 - 299.999 kr.,2.0,0.0,65,0.0,0.0,1782,2,0.5,0.0
|
| 534 |
+
700.000 eller derover,4.0,1.0,51,,0.0,1785,2,0.75,
|
| 535 |
+
500.000 - 599.999 kr.,4.0,1.0,50,1.0,0.0,1786,2,0.75,0.0
|
| 536 |
+
700.000 eller derover,4.0,0.0,56,,0.0,1787,2,1.0,
|
| 537 |
+
700.000 eller derover,5.0,1.0,33,0.0,0.0,1789,2,0.25,0.0
|
| 538 |
+
500.000 - 599.999 kr.,2.0,1.0,60,0.0,0.0,1793,2,0.5,0.0
|
| 539 |
+
700.000 eller derover,4.0,1.0,35,0.0,0.0,1796,2,0.5,0.0
|
| 540 |
+
300.000 - 399.999 kr.,2.0,1.0,63,1.0,0.0,1800,2,0.75,0.0
|
| 541 |
+
300.000 - 399.999 kr.,2.0,1.0,39,,0.0,1806,2,0.25,
|
| 542 |
+
600.000 - 699.999 kr.,5.0,1.0,35,,0.0,1814,2,0.5,
|
| 543 |
+
300.000 - 399.999 kr.,5.0,1.0,31,1.0,0.0,1816,2,0.5,0.0
|
| 544 |
+
700.000 eller derover,2.0,1.0,30,1.0,0.0,1817,2,0.75,0.0
|
| 545 |
+
500.000 - 599.999 kr.,5.0,1.0,63,0.0,0.0,1822,2,0.75,0.0
|
| 546 |
+
700.000 eller derover,4.0,1.0,48,,0.0,1824,2,0.5,
|
| 547 |
+
Onsker ikke at oplyse,4.0,1.0,60,1.0,0.0,1831,2,0.5,0.0
|
| 548 |
+
400.000 - 499.999 kr.,4.0,1.0,39,,0.0,1836,2,0.25,
|
| 549 |
+
700.000 eller derover,3.0,1.0,23,,0.0,1844,2,0.5,
|
| 550 |
+
400.000 - 499.999 kr.,4.0,1.0,64,,0.0,1846,2,0.75,
|
| 551 |
+
500.000 - 599.999 kr.,4.0,1.0,46,,0.0,1854,2,0.5,
|
| 552 |
+
600.000 - 699.999 kr.,4.0,1.0,38,0.0,0.0,1855,2,0.75,0.0
|
| 553 |
+
400.000 - 499.999 kr.,4.0,0.0,42,,0.0,1869,2,0.5,
|
| 554 |
+
500.000 - 599.999 kr.,2.0,1.0,67,,0.0,1871,2,0.5,
|
| 555 |
+
600.000 - 699.999 kr.,3.0,1.0,52,1.0,0.0,1877,2,0.5,0.0
|
| 556 |
+
Onsker ikke at oplyse,4.0,1.0,41,0.0,0.0,1882,2,0.75,0.0
|
| 557 |
+
200.000 - 299.999 kr.,5.0,1.0,54,0.0,0.0,1889,2,0.5,0.0
|
| 558 |
+
400.000 - 499.999 kr.,2.0,1.0,10,1.0,0.0,1891,2,0.5,0.0
|
| 559 |
+
Onsker ikke at oplyse,4.0,0.0,29,,0.0,1897,2,1.0,
|
| 560 |
+
500.000 - 599.999 kr.,4.0,0.0,50,1.0,0.0,1898,2,1.0,0.0
|
| 561 |
+
700.000 eller derover,4.0,0.0,61,,0.0,1901,2,0.75,
|
| 562 |
+
Onsker ikke at oplyse,5.0,1.0,41,0.0,0.0,1902,2,0.75,0.0
|
| 563 |
+
300.000 - 399.999 kr.,4.0,0.0,48,1.0,0.0,1903,2,0.75,0.0
|
| 564 |
+
700.000 eller derover,2.0,1.0,53,1.0,0.0,1907,2,1.0,0.0
|
| 565 |
+
400.000 - 499.999 kr.,4.0,0.0,62,1.0,0.0,1924,2,0.5,0.0
|
| 566 |
+
100.000 - 199.999 kr.,4.0,1.0,25,,0.0,1925,2,0.75,
|
| 567 |
+
600.000 - 699.999 kr.,4.0,1.0,52,0.0,0.0,1927,2,0.5,0.0
|
| 568 |
+
Onsker ikke at oplyse,4.0,1.0,33,1.0,0.0,1928,2,0.5,0.0
|
| 569 |
+
700.000 eller derover,5.0,1.0,36,1.0,0.0,1933,2,0.5,0.0
|
| 570 |
+
700.000 eller derover,4.0,1.0,54,,0.0,1935,2,0.5,
|
| 571 |
+
300.000 - 399.999 kr.,4.0,0.0,65,0.0,0.0,1937,2,0.5,0.0
|
| 572 |
+
700.000 eller derover,5.0,1.0,46,0.0,0.0,1938,2,0.25,0.0
|
| 573 |
+
700.000 eller derover,4.0,1.0,49,1.0,0.0,1939,2,0.5,0.0
|
| 574 |
+
400.000 - 499.999 kr.,4.0,0.0,61,0.0,0.0,1941,2,0.75,0.0
|
| 575 |
+
600.000 - 699.999 kr.,2.0,1.0,55,1.0,0.0,1944,2,0.5,0.0
|
| 576 |
+
700.000 eller derover,4.0,0.0,57,1.0,0.0,1950,2,1.0,0.0
|
| 577 |
+
400.000 - 499.999 kr.,4.0,1.0,51,0.0,0.0,1951,2,0.75,0.0
|
| 578 |
+
500.000 - 599.999 kr.,4.0,1.0,42,0.0,0.0,1957,2,0.5,0.0
|
| 579 |
+
200.000 - 299.999 kr.,4.0,0.0,61,,0.0,1960,2,0.5,
|
| 580 |
+
200.000 - 299.999 kr.,2.0,1.0,64,0.0,0.0,1961,2,0.5,0.0
|
| 581 |
+
700.000 eller derover,4.0,1.0,50,0.0,0.0,1967,2,0.75,0.0
|
| 582 |
+
400.000 - 499.999 kr.,5.0,1.0,63,1.0,0.0,1974,2,0.75,0.0
|
| 583 |
+
600.000 - 699.999 kr.,3.0,1.0,53,,0.0,1976,2,0.5,
|
| 584 |
+
700.000 eller derover,5.0,1.0,48,0.0,0.0,1985,2,0.25,0.0
|
| 585 |
+
500.000 - 599.999 kr.,4.0,0.0,50,1.0,0.0,1987,2,0.5,0.0
|
| 586 |
+
700.000 eller derover,5.0,0.0,61,1.0,0.0,1988,2,0.75,0.0
|
| 587 |
+
200.000 - 299.999 kr.,4.0,0.0,76,0.0,0.0,1993,2,0.75,0.0
|
| 588 |
+
400.000 - 499.999 kr.,4.0,1.0,58,0.0,0.0,1995,2,0.75,0.0
|
| 589 |
+
Onsker ikke at oplyse,4.0,1.0,61,0.0,0.0,1996,2,0.25,0.0
|
| 590 |
+
500.000 - 599.999 kr.,4.0,1.0,56,0.0,0.0,1999,2,0.5,0.0
|
| 591 |
+
700.000 eller derover,4.0,1.0,40,0.0,0.0,2000,2,0.5,0.0
|
| 592 |
+
100.000 - 199.999 kr.,1.0,1.0,48,1.0,0.0,2006,2,0.0,0.0
|
| 593 |
+
Onsker ikke at oplyse,3.0,0.0,40,0.0,0.0,2009,2,0.75,0.0
|
| 594 |
+
600.000 - 699.999 kr.,3.0,1.0,51,1.0,0.0,2011,2,0.75,0.0
|
| 595 |
+
700.000 eller derover,4.0,1.0,45,0.0,0.0,2017,2,0.5,0.0
|
| 596 |
+
Onsker ikke at oplyse,5.0,0.0,39,1.0,0.0,2022,2,0.5,0.0
|
| 597 |
+
700.000 eller derover,4.0,0.0,45,,0.0,2025,2,0.75,
|
| 598 |
+
100.000 - 199.999 kr.,2.0,1.0,57,1.0,0.0,2033,2,0.25,0.0
|
| 599 |
+
700.000 eller derover,5.0,1.0,57,1.0,0.0,2035,2,0.5,0.0
|
| 600 |
+
400.000 - 499.999 kr.,4.0,0.0,59,1.0,0.0,2039,2,0.75,0.0
|
| 601 |
+
400.000 - 499.999 kr.,4.0,1.0,45,,0.0,2040,2,0.25,
|
| 602 |
+
Onsker ikke at oplyse,4.0,0.0,59,1.0,0.0,2048,2,0.75,0.0
|
| 603 |
+
500.000 - 599.999 kr.,2.0,1.0,52,,0.0,2052,2,0.5,
|
| 604 |
+
400.000 - 499.999 kr.,2.0,0.0,41,0.0,0.0,2056,2,0.5,0.0
|
| 605 |
+
600.000 - 699.999 kr.,4.0,0.0,46,1.0,0.0,2060,2,0.5,0.0
|
| 606 |
+
700.000 eller derover,5.0,0.0,42,1.0,0.0,2066,2,0.5,0.0
|
| 607 |
+
400.000 - 499.999 kr.,5.0,0.0,34,1.0,0.0,2068,2,1.0,0.0
|
| 608 |
+
300.000 - 399.999 kr.,5.0,0.0,49,,0.0,2073,2,0.5,
|
| 609 |
+
300.000 - 399.999 kr.,5.0,1.0,53,,0.0,2074,2,0.5,
|
| 610 |
+
100.000 - 199.999 kr.,4.0,1.0,25,0.0,0.0,2075,2,1.0,0.0
|
| 611 |
+
400.000 - 499.999 kr.,5.0,0.0,41,1.0,0.0,2076,2,0.25,0.0
|
| 612 |
+
400.000 - 499.999 kr.,2.0,0.0,54,1.0,0.0,2077,2,1.0,0.0
|
| 613 |
+
300.000 - 399.999 kr.,4.0,1.0,32,0.0,0.0,2083,2,0.5,0.0
|
| 614 |
+
700.000 eller derover,4.0,1.0,34,0.0,0.0,2085,2,0.5,0.0
|
| 615 |
+
100.000 - 199.999 kr.,3.0,1.0,22,1.0,0.0,2090,2,0.5,0.0
|
| 616 |
+
200.000 - 299.999 kr.,2.0,0.0,51,1.0,0.0,2094,2,0.75,0.0
|
| 617 |
+
Onsker ikke at oplyse,5.0,1.0,57,,0.0,2114,2,0.75,
|
| 618 |
+
300.000 - 399.999 kr.,4.0,1.0,66,,0.0,2118,2,0.5,
|
| 619 |
+
500.000 - 599.999 kr.,5.0,1.0,34,0.0,0.0,2128,2,1.0,0.0
|
| 620 |
+
400.000 - 499.999 kr.,4.0,1.0,60,0.0,0.0,2130,2,0.0,0.0
|
| 621 |
+
400.000 - 499.999 kr.,4.0,0.0,57,0.0,0.0,2135,2,0.5,0.0
|
| 622 |
+
100.000 - 199.999 kr.,3.0,1.0,42,,0.0,2139,2,0.75,
|
| 623 |
+
700.000 eller derover,5.0,1.0,37,0.0,0.0,2144,2,0.75,0.0
|
| 624 |
+
Onsker ikke at oplyse,4.0,0.0,50,1.0,0.0,2145,2,0.75,0.0
|
| 625 |
+
500.000 - 599.999 kr.,5.0,1.0,71,1.0,0.0,2146,2,0.5,0.0
|
| 626 |
+
300.000 - 399.999 kr.,1.0,0.0,63,0.0,0.0,2147,2,0.75,0.0
|
| 627 |
+
500.000 - 599.999 kr.,2.0,0.0,56,1.0,0.0,2148,2,1.0,0.0
|
| 628 |
+
200.000 - 299.999 kr.,4.0,1.0,65,0.0,0.0,2150,2,0.5,0.0
|
| 629 |
+
300.000 - 399.999 kr.,2.0,1.0,29,,0.0,2155,2,0.75,
|
| 630 |
+
300.000 - 399.999 kr.,5.0,1.0,45,0.0,0.0,2156,2,0.5,0.0
|
| 631 |
+
Onsker ikke at oplyse,5.0,0.0,53,0.0,0.0,2159,2,0.5,0.0
|
| 632 |
+
400.000 - 499.999 kr.,3.0,0.0,36,,0.0,2166,2,0.5,
|
| 633 |
+
400.000 - 499.999 kr.,4.0,0.0,52,,0.0,2171,2,0.5,
|
| 634 |
+
400.000 - 499.999 kr.,5.0,1.0,32,,0.0,2172,2,0.75,
|
| 635 |
+
600.000 - 699.999 kr.,3.0,1.0,52,,0.0,2177,2,0.5,
|
| 636 |
+
200.000 - 299.999 kr.,5.0,0.0,58,0.0,0.0,2178,2,0.75,0.0
|
| 637 |
+
600.000 - 699.999 kr.,4.0,1.0,52,1.0,0.0,2181,2,0.25,0.0
|
| 638 |
+
200.000 - 299.999 kr.,4.0,0.0,56,1.0,0.0,2184,2,1.0,0.0
|
| 639 |
+
400.000 - 499.999 kr.,4.0,1.0,61,1.0,0.0,2186,2,0.25,0.0
|
| 640 |
+
700.000 eller derover,4.0,1.0,56,1.0,0.0,2198,2,0.75,0.0
|
| 641 |
+
100.000 - 199.999 kr.,1.0,0.0,64,,0.0,2215,2,0.75,
|
| 642 |
+
Onsker ikke at oplyse,4.0,0.0,47,1.0,0.0,2219,2,0.75,0.0
|
| 643 |
+
500.000 - 599.999 kr.,4.0,0.0,49,1.0,0.0,2220,2,0.75,0.0
|
| 644 |
+
Indtil 99.999 kr.,3.0,0.0,22,1.0,0.0,2224,2,0.5,0.0
|
| 645 |
+
Onsker ikke at oplyse,2.0,1.0,62,0.0,0.0,2226,2,0.5,0.0
|
| 646 |
+
Onsker ikke at oplyse,2.0,1.0,53,1.0,0.0,2228,2,0.75,0.0
|
| 647 |
+
300.000 - 399.999 kr.,3.0,1.0,58,1.0,0.0,2230,2,0.75,0.0
|
| 648 |
+
400.000 - 499.999 kr.,2.0,1.0,33,,0.0,2231,2,0.5,
|
| 649 |
+
100.000 - 199.999 kr.,3.0,0.0,21,1.0,0.0,2234,2,0.75,0.0
|
| 650 |
+
600.000 - 699.999 kr.,4.0,1.0,57,1.0,0.0,2242,2,0.25,0.0
|
| 651 |
+
400.000 - 499.999 kr.,5.0,1.0,66,1.0,0.0,2247,2,1.0,0.0
|
| 652 |
+
200.000 - 299.999 kr.,4.0,0.0,39,1.0,0.0,2248,2,0.5,0.0
|
| 653 |
+
700.000 eller derover,2.0,0.0,47,1.0,0.0,2249,2,0.5,0.0
|
| 654 |
+
700.000 eller derover,5.0,0.0,39,,0.0,2251,2,0.5,
|
| 655 |
+
100.000 - 199.999 kr.,1.0,1.0,60,,0.0,2254,2,0.75,
|
| 656 |
+
700.000 eller derover,5.0,0.0,40,0.0,0.0,2255,2,0.5,0.0
|
| 657 |
+
700.000 eller derover,4.0,1.0,55,,0.0,2256,2,0.75,
|
| 658 |
+
400.000 - 499.999 kr.,5.0,0.0,58,1.0,0.0,2257,2,0.5,0.0
|
| 659 |
+
300.000 - 399.999 kr.,5.0,1.0,28,,0.0,2262,2,0.5,
|
| 660 |
+
200.000 - 299.999 kr.,4.0,1.0,66,1.0,0.0,2266,2,1.0,0.0
|
| 661 |
+
700.000 eller derover,4.0,1.0,57,0.0,0.0,2267,2,0.5,0.0
|
| 662 |
+
500.000 - 599.999 kr.,4.0,0.0,29,1.0,0.0,2274,2,0.5,0.0
|
| 663 |
+
200.000 - 299.999 kr.,4.0,0.0,49,1.0,0.0,2275,2,0.75,0.0
|
| 664 |
+
500.000 - 599.999 kr.,3.0,1.0,53,,0.0,2282,2,0.75,
|
| 665 |
+
400.000 - 499.999 kr.,3.0,0.0,20,1.0,0.0,2283,2,0.75,0.0
|
| 666 |
+
Onsker ikke at oplyse,3.0,1.0,18,0.0,0.0,2286,2,0.25,0.0
|
| 667 |
+
300.000 - 399.999 kr.,4.0,0.0,66,1.0,0.0,2290,2,0.75,0.0
|
| 668 |
+
100.000 - 199.999 kr.,1.0,1.0,80,1.0,0.0,2291,2,0.5,0.0
|
| 669 |
+
300.000 - 399.999 kr.,1.0,0.0,40,,0.0,2293,2,0.75,
|
| 670 |
+
500.000 - 599.999 kr.,4.0,1.0,58,0.0,0.0,2297,2,1.0,0.0
|
| 671 |
+
300.000 - 399.999 kr.,4.0,0.0,63,0.0,0.0,2298,2,0.5,0.0
|
| 672 |
+
700.000 eller derover,3.0,0.0,44,1.0,0.0,2303,2,0.5,0.0
|
| 673 |
+
Onsker ikke at oplyse,1.0,1.0,53,1.0,0.0,2304,2,0.75,0.0
|
| 674 |
+
100.000 - 199.999 kr.,4.0,1.0,68,,0.0,2306,2,0.5,
|
| 675 |
+
600.000 - 699.999 kr.,2.0,1.0,51,0.0,0.0,2316,2,0.5,0.0
|
| 676 |
+
300.000 - 399.999 kr.,4.0,1.0,74,0.0,0.0,2317,2,0.5,0.0
|
| 677 |
+
Indtil 99.999 kr.,3.0,1.0,24,,0.0,2322,2,1.0,
|
| 678 |
+
300.000 - 399.999 kr.,4.0,0.0,58,1.0,0.0,2324,2,0.5,0.0
|
| 679 |
+
400.000 - 499.999 kr.,1.0,1.0,62,,0.0,2330,2,0.5,
|
| 680 |
+
300.000 - 399.999 kr.,5.0,1.0,54,1.0,0.0,2333,2,0.5,0.0
|
| 681 |
+
400.000 - 499.999 kr.,4.0,1.0,30,,0.0,2335,2,0.5,
|
| 682 |
+
700.000 eller derover,5.0,1.0,45,0.0,0.0,2340,2,0.75,0.0
|
| 683 |
+
700.000 eller derover,4.0,0.0,46,0.0,0.0,2350,2,0.75,0.0
|
| 684 |
+
600.000 - 699.999 kr.,5.0,1.0,28,0.0,0.0,2352,2,0.75,0.0
|
| 685 |
+
700.000 eller derover,4.0,0.0,44,1.0,0.0,2353,2,0.5,0.0
|
| 686 |
+
400.000 - 499.999 kr.,2.0,1.0,49,,0.0,2354,2,0.75,
|
| 687 |
+
300.000 - 399.999 kr.,4.0,0.0,59,0.0,0.0,2356,2,0.25,0.0
|
| 688 |
+
200.000 - 299.999 kr.,2.0,0.0,51,1.0,0.0,2359,2,1.0,0.0
|
| 689 |
+
500.000 - 599.999 kr.,2.0,1.0,42,,0.0,2363,2,0.5,
|
| 690 |
+
400.000 - 499.999 kr.,4.0,1.0,42,1.0,0.0,2364,2,0.25,0.0
|
| 691 |
+
300.000 - 399.999 kr.,2.0,0.0,47,1.0,0.0,2366,2,0.75,0.0
|
| 692 |
+
300.000 - 399.999 kr.,3.0,1.0,35,0.0,0.0,2367,2,0.5,0.0
|
| 693 |
+
700.000 eller derover,4.0,1.0,54,0.0,0.0,2371,2,1.0,0.0
|
| 694 |
+
300.000 - 399.999 kr.,4.0,1.0,42,,0.0,2372,2,0.5,
|
| 695 |
+
400.000 - 499.999 kr.,3.0,1.0,51,0.0,0.0,2374,2,0.75,0.0
|
| 696 |
+
700.000 eller derover,5.0,1.0,56,0.0,0.0,2406,2,0.5,0.0
|
| 697 |
+
400.000 - 499.999 kr.,5.0,0.0,44,,0.0,2411,2,0.5,
|
| 698 |
+
700.000 eller derover,5.0,1.0,62,,0.0,2415,2,0.5,
|
| 699 |
+
300.000 - 399.999 kr.,2.0,1.0,30,,0.0,2416,2,0.25,
|
| 700 |
+
700.000 eller derover,5.0,1.0,51,1.0,0.0,2417,2,0.5,0.0
|
| 701 |
+
300.000 - 399.999 kr.,4.0,1.0,52,,0.0,2425,2,0.25,
|
| 702 |
+
500.000 - 599.999 kr.,2.0,1.0,55,1.0,0.0,2427,2,0.75,0.0
|
| 703 |
+
700.000 eller derover,5.0,1.0,50,0.0,0.0,2428,2,0.75,0.0
|
| 704 |
+
500.000 - 599.999 kr.,4.0,1.0,42,0.0,0.0,2435,2,0.5,0.0
|
| 705 |
+
500.000 - 599.999 kr.,4.0,1.0,62,1.0,0.0,2442,2,0.25,0.0
|
| 706 |
+
700.000 eller derover,4.0,1.0,50,0.0,0.0,2445,2,0.25,0.0
|
| 707 |
+
600.000 - 699.999 kr.,5.0,0.0,45,1.0,0.0,2450,2,0.75,0.0
|
| 708 |
+
500.000 - 599.999 kr.,1.0,1.0,68,1.0,0.0,2451,2,1.0,0.0
|
| 709 |
+
200.000 - 299.999 kr.,3.0,0.0,32,,0.0,2457,2,0.75,
|
| 710 |
+
500.000 - 599.999 kr.,4.0,0.0,29,,0.0,2458,2,1.0,
|
| 711 |
+
700.000 eller derover,2.0,1.0,46,,0.0,2459,2,0.75,
|
| 712 |
+
300.000 - 399.999 kr.,2.0,1.0,55,1.0,0.0,2462,2,1.0,0.0
|
| 713 |
+
600.000 - 699.999 kr.,4.0,1.0,34,1.0,0.0,2466,2,0.5,0.0
|
| 714 |
+
700.000 eller derover,3.0,1.0,49,0.0,0.0,2467,2,0.75,0.0
|
| 715 |
+
600.000 - 699.999 kr.,4.0,1.0,42,1.0,0.0,2470,2,0.5,0.0
|
| 716 |
+
100.000 - 199.999 kr.,1.0,1.0,65,,0.0,2479,2,0.5,
|
| 717 |
+
700.000 eller derover,2.0,1.0,48,1.0,0.0,2480,2,0.5,0.0
|
| 718 |
+
700.000 eller derover,4.0,0.0,43,0.0,0.0,2482,2,0.25,0.0
|
| 719 |
+
Onsker ikke at oplyse,4.0,0.0,63,0.0,0.0,2483,2,0.5,0.0
|
| 720 |
+
600.000 - 699.999 kr.,5.0,0.0,37,0.0,0.0,2485,2,0.5,0.0
|
| 721 |
+
200.000 - 299.999 kr.,1.0,1.0,52,1.0,0.0,2486,2,0.5,0.0
|
| 722 |
+
400.000 - 499.999 kr.,5.0,0.0,50,,0.0,2489,2,1.0,
|
| 723 |
+
Onsker ikke at oplyse,5.0,1.0,31,,0.0,2491,2,0.5,
|
| 724 |
+
600.000 - 699.999 kr.,2.0,0.0,37,0.0,0.0,2493,2,0.25,0.0
|
| 725 |
+
300.000 - 399.999 kr.,2.0,1.0,64,,0.0,2494,2,0.5,
|
| 726 |
+
400.000 - 499.999 kr.,4.0,1.0,27,0.0,0.0,2498,2,1.0,0.0
|
| 727 |
+
700.000 eller derover,5.0,1.0,41,,0.0,2506,2,0.75,
|
| 728 |
+
400.000 - 499.999 kr.,4.0,1.0,41,,0.0,2507,2,0.25,
|
| 729 |
+
700.000 eller derover,5.0,0.0,33,1.0,0.0,2508,2,0.25,0.0
|
| 730 |
+
700.000 eller derover,4.0,1.0,47,,0.0,2509,2,0.75,
|
| 731 |
+
400.000 - 499.999 kr.,4.0,0.0,51,,0.0,2511,2,0.75,
|
| 732 |
+
300.000 - 399.999 kr.,4.0,1.0,65,1.0,0.0,2512,2,0.75,0.0
|
| 733 |
+
Onsker ikke at oplyse,4.0,1.0,62,,0.0,2513,2,0.0,
|
| 734 |
+
300.000 - 399.999 kr.,4.0,1.0,67,0.0,0.0,2516,2,0.25,0.0
|
| 735 |
+
300.000 - 399.999 kr.,4.0,0.0,42,0.0,0.0,2517,2,0.5,0.0
|
| 736 |
+
700.000 eller derover,5.0,1.0,32,1.0,0.0,2519,2,0.5,0.0
|
| 737 |
+
100.000 - 199.999 kr.,5.0,0.0,29,,0.0,2521,2,0.5,
|
| 738 |
+
200.000 - 299.999 kr.,2.0,1.0,24,0.0,0.0,2525,2,0.25,0.0
|
| 739 |
+
300.000 - 399.999 kr.,4.0,1.0,52,1.0,0.0,2530,2,1.0,0.0
|
| 740 |
+
400.000 - 499.999 kr.,4.0,1.0,57,1.0,0.0,2531,2,0.75,0.0
|
| 741 |
+
700.000 eller derover,5.0,1.0,58,,0.0,2534,2,0.25,
|
| 742 |
+
Onsker ikke at oplyse,1.0,1.0,68,0.0,0.0,2537,2,1.0,0.0
|
| 743 |
+
700.000 eller derover,4.0,1.0,59,,0.0,2546,2,0.5,
|
| 744 |
+
600.000 - 699.999 kr.,2.0,1.0,51,0.0,0.0,2548,2,0.75,0.0
|
| 745 |
+
Onsker ikke at oplyse,2.0,1.0,49,1.0,0.0,2553,2,0.5,0.0
|
| 746 |
+
600.000 - 699.999 kr.,5.0,1.0,35,0.0,0.0,2556,2,0.5,0.0
|
| 747 |
+
600.000 - 699.999 kr.,5.0,1.0,63,0.0,0.0,2561,2,0.75,0.0
|
| 748 |
+
400.000 - 499.999 kr.,5.0,1.0,54,,0.0,2566,2,0.75,
|
| 749 |
+
700.000 eller derover,4.0,0.0,36,0.0,0.0,2572,2,0.75,0.0
|
| 750 |
+
Onsker ikke at oplyse,5.0,1.0,59,,0.0,2582,2,0.5,
|
| 751 |
+
600.000 - 699.999 kr.,4.0,0.0,51,,0.0,2584,2,1.0,
|
| 752 |
+
400.000 - 499.999 kr.,1.0,1.0,64,,0.0,2588,2,0.5,
|
| 753 |
+
300.000 - 399.999 kr.,2.0,1.0,45,1.0,0.0,2589,2,0.5,0.0
|
| 754 |
+
700.000 eller derover,4.0,0.0,52,,0.0,2599,2,0.5,
|
| 755 |
+
300.000 - 399.999 kr.,4.0,0.0,44,0.0,0.0,2601,2,0.25,0.0
|
| 756 |
+
200.000 - 299.999 kr.,4.0,1.0,55,1.0,0.0,2608,2,0.25,0.0
|
| 757 |
+
300.000 - 399.999 kr.,1.0,0.0,58,0.0,0.0,2610,2,0.75,0.0
|
| 758 |
+
200.000 - 299.999 kr.,5.0,0.0,31,1.0,0.0,2612,2,0.5,0.0
|
| 759 |
+
400.000 - 499.999 kr.,5.0,1.0,57,0.0,0.0,2615,2,0.25,0.0
|
| 760 |
+
600.000 - 699.999 kr.,4.0,1.0,63,,0.0,2617,2,0.5,
|
| 761 |
+
400.000 - 499.999 kr.,5.0,1.0,42,1.0,0.0,2620,2,0.25,0.0
|
| 762 |
+
600.000 - 699.999 kr.,2.0,0.0,44,0.0,0.0,2623,2,0.5,0.0
|
| 763 |
+
400.000 - 499.999 kr.,2.0,1.0,43,0.0,0.0,2628,2,0.25,0.0
|
| 764 |
+
200.000 - 299.999 kr.,3.0,1.0,28,,0.0,2629,2,0.25,
|
| 765 |
+
700.000 eller derover,1.0,1.0,43,1.0,0.0,2634,2,1.0,0.0
|
| 766 |
+
100.000 - 199.999 kr.,2.0,0.0,39,0.0,0.0,2637,2,1.0,0.0
|
| 767 |
+
400.000 - 499.999 kr.,5.0,0.0,2,1.0,0.0,2648,2,0.5,0.0
|
| 768 |
+
600.000 - 699.999 kr.,4.0,1.0,54,,0.0,2653,2,0.75,
|
| 769 |
+
600.000 - 699.999 kr.,5.0,1.0,55,1.0,0.0,2660,2,0.5,0.0
|
| 770 |
+
500.000 - 599.999 kr.,4.0,1.0,50,,0.0,2663,2,0.5,
|
| 771 |
+
100.000 - 199.999 kr.,2.0,1.0,67,1.0,0.0,2670,2,0.5,0.0
|
| 772 |
+
700.000 eller derover,4.0,1.0,41,,0.0,2672,2,0.5,
|
| 773 |
+
700.000 eller derover,5.0,0.0,55,1.0,0.0,2675,2,0.75,0.0
|
| 774 |
+
700.000 eller derover,2.0,1.0,62,1.0,0.0,2677,2,0.5,0.0
|
| 775 |
+
500.000 - 599.999 kr.,4.0,1.0,48,1.0,0.0,2678,2,0.5,0.0
|
| 776 |
+
600.000 - 699.999 kr.,4.0,1.0,56,1.0,0.0,2686,2,0.75,0.0
|
| 777 |
+
200.000 - 299.999 kr.,2.0,1.0,50,1.0,0.0,2692,2,0.75,0.0
|
| 778 |
+
300.000 - 399.999 kr.,4.0,0.0,55,1.0,0.0,2693,2,0.5,0.0
|
| 779 |
+
700.000 eller derover,2.0,1.0,53,0.0,0.0,2697,2,0.25,0.0
|
| 780 |
+
700.000 eller derover,1.0,1.0,43,1.0,0.0,2699,2,0.25,0.0
|
| 781 |
+
500.000 - 599.999 kr.,4.0,1.0,68,1.0,0.0,2700,2,0.5,0.0
|
| 782 |
+
500.000 - 599.999 kr.,2.0,1.0,63,1.0,0.0,2701,2,0.75,0.0
|
| 783 |
+
200.000 - 299.999 kr.,2.0,1.0,35,,0.0,2709,2,0.5,
|
| 784 |
+
200.000 - 299.999 kr.,3.0,0.0,60,1.0,0.0,2711,2,0.75,0.0
|
| 785 |
+
100.000 - 199.999 kr.,3.0,1.0,24,0.0,0.0,2712,2,0.5,0.0
|
| 786 |
+
400.000 - 499.999 kr.,4.0,1.0,73,1.0,0.0,2717,2,0.75,0.0
|
| 787 |
+
200.000 - 299.999 kr.,1.0,0.0,53,1.0,0.0,2720,2,0.5,0.0
|
| 788 |
+
400.000 - 499.999 kr.,4.0,0.0,45,1.0,0.0,2722,2,0.5,0.0
|
| 789 |
+
600.000 - 699.999 kr.,5.0,1.0,33,0.0,0.0,2723,2,0.5,0.0
|
| 790 |
+
400.000 - 499.999 kr.,3.0,0.0,43,,0.0,2725,2,0.75,
|
| 791 |
+
600.000 - 699.999 kr.,4.0,1.0,56,,0.0,2737,2,0.75,
|
| 792 |
+
400.000 - 499.999 kr.,4.0,1.0,45,,0.0,2741,2,0.5,
|
| 793 |
+
300.000 - 399.999 kr.,2.0,1.0,59,1.0,0.0,2742,2,0.75,0.0
|
| 794 |
+
700.000 eller derover,4.0,0.0,54,0.0,0.0,2751,2,1.0,0.0
|
| 795 |
+
200.000 - 299.999 kr.,4.0,1.0,68,,0.0,2753,2,0.5,
|
| 796 |
+
400.000 - 499.999 kr.,5.0,1.0,65,0.0,0.0,2755,2,0.75,0.0
|
| 797 |
+
400.000 - 499.999 kr.,4.0,1.0,64,0.0,0.0,2765,2,0.5,0.0
|
| 798 |
+
500.000 - 599.999 kr.,4.0,1.0,51,0.0,0.0,2767,2,0.0,0.0
|
| 799 |
+
400.000 - 499.999 kr.,4.0,0.0,58,,0.0,2769,2,0.5,
|
| 800 |
+
600.000 - 699.999 kr.,4.0,1.0,55,0.0,0.0,2770,2,0.5,0.0
|
| 801 |
+
300.000 - 399.999 kr.,5.0,1.0,55,1.0,0.0,2771,2,0.75,0.0
|
| 802 |
+
700.000 eller derover,4.0,0.0,50,0.0,0.0,2772,2,0.25,0.0
|
| 803 |
+
300.000 - 399.999 kr.,5.0,1.0,28,1.0,0.0,2774,2,0.75,0.0
|
| 804 |
+
700.000 eller derover,4.0,0.0,52,1.0,0.0,2776,2,0.5,0.0
|
| 805 |
+
300.000 - 399.999 kr.,4.0,0.0,44,1.0,0.0,2779,2,0.5,0.0
|
| 806 |
+
Onsker ikke at oplyse,4.0,1.0,60,,0.0,2780,2,0.75,
|
| 807 |
+
400.000 - 499.999 kr.,4.0,1.0,65,1.0,0.0,2782,2,0.25,0.0
|
| 808 |
+
700.000 eller derover,4.0,0.0,41,,0.0,2785,2,0.25,
|
| 809 |
+
300.000 - 399.999 kr.,4.0,1.0,65,0.0,0.0,2786,2,1.0,0.0
|
| 810 |
+
500.000 - 599.999 kr.,2.0,1.0,65,,0.0,2792,2,0.75,
|
| 811 |
+
400.000 - 499.999 kr.,1.0,1.0,39,,0.0,2793,2,0.5,
|
| 812 |
+
Onsker ikke at oplyse,5.0,1.0,47,1.0,0.0,2794,2,0.75,0.0
|
| 813 |
+
500.000 - 599.999 kr.,4.0,0.0,62,,0.0,2799,2,0.75,
|
| 814 |
+
600.000 - 699.999 kr.,4.0,1.0,61,1.0,0.0,2801,2,0.25,0.0
|
| 815 |
+
700.000 eller derover,3.0,1.0,48,1.0,0.0,2807,2,0.75,0.0
|
| 816 |
+
Onsker ikke at oplyse,4.0,1.0,48,,0.0,2811,2,0.5,
|
| 817 |
+
700.000 eller derover,5.0,0.0,51,1.0,0.0,2813,2,0.75,0.0
|
| 818 |
+
200.000 - 299.999 kr.,2.0,1.0,70,,0.0,2818,2,0.75,
|
| 819 |
+
400.000 - 499.999 kr.,4.0,1.0,52,,0.0,2824,2,0.5,
|
| 820 |
+
100.000 - 199.999 kr.,5.0,1.0,58,1.0,0.0,2830,2,0.5,0.0
|
| 821 |
+
500.000 - 599.999 kr.,5.0,1.0,43,,0.0,2845,2,0.75,
|
| 822 |
+
Onsker ikke at oplyse,4.0,1.0,41,0.0,0.0,2847,2,0.5,0.0
|
| 823 |
+
300.000 - 399.999 kr.,2.0,1.0,49,1.0,0.0,2848,2,0.75,0.0
|
| 824 |
+
700.000 eller derover,5.0,1.0,52,1.0,0.0,2849,2,0.5,0.0
|
| 825 |
+
600.000 - 699.999 kr.,2.0,1.0,52,,0.0,2851,2,0.5,
|
| 826 |
+
300.000 - 399.999 kr.,4.0,1.0,42,0.0,0.0,2852,2,0.25,0.0
|
| 827 |
+
700.000 eller derover,5.0,1.0,79,0.0,0.0,2853,2,0.25,0.0
|
| 828 |
+
100.000 - 199.999 kr.,2.0,0.0,62,,0.0,2855,2,0.75,
|
| 829 |
+
400.000 - 499.999 kr.,4.0,1.0,65,0.0,0.0,2856,2,0.75,0.0
|
| 830 |
+
700.000 eller derover,4.0,0.0,37,0.0,0.0,2860,2,0.5,0.0
|
| 831 |
+
Onsker ikke at oplyse,3.0,1.0,47,,0.0,2863,2,0.75,
|
| 832 |
+
600.000 - 699.999 kr.,3.0,0.0,56,1.0,0.0,2864,2,0.25,0.0
|
| 833 |
+
600.000 - 699.999 kr.,4.0,1.0,50,,0.0,2865,2,1.0,
|
| 834 |
+
700.000 eller derover,5.0,1.0,42,,0.0,2869,2,0.5,
|
| 835 |
+
400.000 - 499.999 kr.,1.0,1.0,59,0.0,0.0,2874,2,0.5,0.0
|
| 836 |
+
500.000 - 599.999 kr.,5.0,1.0,72,1.0,0.0,2876,2,0.5,0.0
|
| 837 |
+
700.000 eller derover,2.0,0.0,58,0.0,0.0,2879,2,1.0,0.0
|
| 838 |
+
700.000 eller derover,3.0,1.0,54,,0.0,2880,2,0.5,
|
| 839 |
+
700.000 eller derover,4.0,0.0,54,1.0,0.0,2883,2,0.5,0.0
|
| 840 |
+
400.000 - 499.999 kr.,4.0,0.0,64,1.0,0.0,2884,2,0.75,0.0
|
| 841 |
+
700.000 eller derover,2.0,1.0,49,1.0,0.0,2886,2,0.75,0.0
|
| 842 |
+
600.000 - 699.999 kr.,4.0,0.0,51,1.0,0.0,2887,2,1.0,0.0
|
| 843 |
+
700.000 eller derover,5.0,0.0,34,0.0,0.0,2890,2,0.25,0.0
|
| 844 |
+
700.000 eller derover,4.0,1.0,53,0.0,0.0,2891,2,0.5,0.0
|
| 845 |
+
700.000 eller derover,5.0,0.0,55,1.0,0.0,2892,2,0.75,0.0
|
| 846 |
+
400.000 - 499.999 kr.,4.0,1.0,49,0.0,0.0,2895,2,0.75,0.0
|
| 847 |
+
400.000 - 499.999 kr.,5.0,0.0,27,,0.0,2896,2,0.25,
|
| 848 |
+
500.000 - 599.999 kr.,2.0,0.0,47,0.0,0.0,2900,2,0.75,0.0
|
| 849 |
+
700.000 eller derover,5.0,1.0,35,1.0,1.0,6,3,0.75,1.0
|
| 850 |
+
600.000 - 699.999 kr.,5.0,1.0,57,0.0,1.0,10,3,0.5,0.0
|
| 851 |
+
300.000 - 399.999 kr.,4.0,1.0,42,1.0,1.0,12,3,0.5,1.0
|
| 852 |
+
700.000 eller derover,5.0,1.0,39,0.0,1.0,15,3,0.5,0.0
|
| 853 |
+
500.000 - 599.999 kr.,4.0,0.0,41,,1.0,16,3,0.5,
|
| 854 |
+
Onsker ikke at oplyse,2.0,1.0,47,,1.0,24,3,0.5,
|
| 855 |
+
Onsker ikke at oplyse,3.0,0.0,57,,1.0,27,3,0.25,
|
| 856 |
+
300.000 - 399.999 kr.,4.0,0.0,59,0.0,1.0,31,3,0.5,0.0
|
| 857 |
+
600.000 - 699.999 kr.,2.0,1.0,42,,1.0,32,3,0.5,
|
| 858 |
+
Onsker ikke at oplyse,4.0,1.0,62,0.0,1.0,33,3,0.5,0.0
|
| 859 |
+
Onsker ikke at oplyse,3.0,1.0,61,0.0,1.0,40,3,1.0,0.0
|
| 860 |
+
700.000 eller derover,4.0,0.0,48,0.0,1.0,42,3,0.75,0.0
|
| 861 |
+
700.000 eller derover,4.0,0.0,54,1.0,1.0,44,3,0.5,1.0
|
| 862 |
+
700.000 eller derover,4.0,1.0,55,,1.0,48,3,0.5,
|
| 863 |
+
Onsker ikke at oplyse,3.0,1.0,23,1.0,1.0,50,3,0.5,1.0
|
| 864 |
+
Indtil 99.999 kr.,3.0,1.0,25,,1.0,52,3,0.5,
|
| 865 |
+
700.000 eller derover,3.0,1.0,45,0.0,1.0,55,3,0.5,0.0
|
| 866 |
+
500.000 - 599.999 kr.,5.0,0.0,38,1.0,1.0,59,3,0.75,1.0
|
| 867 |
+
300.000 - 399.999 kr.,4.0,0.0,31,0.0,1.0,64,3,0.5,0.0
|
| 868 |
+
500.000 - 599.999 kr.,5.0,1.0,53,0.0,1.0,65,3,0.5,0.0
|
| 869 |
+
200.000 - 299.999 kr.,3.0,1.0,42,,1.0,69,3,0.75,
|
| 870 |
+
700.000 eller derover,4.0,0.0,42,1.0,1.0,70,3,0.75,1.0
|
| 871 |
+
300.000 - 399.999 kr.,1.0,1.0,45,,1.0,72,3,1.0,
|
| 872 |
+
700.000 eller derover,5.0,0.0,42,,1.0,77,3,0.5,
|
| 873 |
+
600.000 - 699.999 kr.,4.0,1.0,64,1.0,1.0,79,3,0.5,1.0
|
| 874 |
+
700.000 eller derover,5.0,1.0,41,,1.0,81,3,1.0,
|
| 875 |
+
600.000 - 699.999 kr.,4.0,1.0,57,1.0,1.0,85,3,0.5,1.0
|
| 876 |
+
600.000 - 699.999 kr.,2.0,1.0,60,0.0,1.0,87,3,0.5,0.0
|
| 877 |
+
600.000 - 699.999 kr.,5.0,1.0,33,,1.0,89,3,0.5,
|
| 878 |
+
400.000 - 499.999 kr.,4.0,0.0,67,1.0,1.0,90,3,0.75,1.0
|
| 879 |
+
700.000 eller derover,4.0,1.0,63,1.0,1.0,92,3,0.75,1.0
|
| 880 |
+
400.000 - 499.999 kr.,2.0,1.0,54,1.0,1.0,95,3,1.0,1.0
|
| 881 |
+
Onsker ikke at oplyse,5.0,1.0,49,0.0,1.0,98,3,0.5,0.0
|
| 882 |
+
700.000 eller derover,4.0,1.0,51,1.0,1.0,101,3,0.5,1.0
|
| 883 |
+
500.000 - 599.999 kr.,5.0,1.0,84,,1.0,104,3,0.75,
|
| 884 |
+
200.000 - 299.999 kr.,3.0,0.0,45,0.0,1.0,106,3,0.5,0.0
|
| 885 |
+
600.000 - 699.999 kr.,1.0,1.0,59,1.0,1.0,110,3,0.25,1.0
|
| 886 |
+
Onsker ikke at oplyse,4.0,0.0,63,1.0,1.0,114,3,0.25,1.0
|
| 887 |
+
600.000 - 699.999 kr.,5.0,1.0,63,,1.0,121,3,0.5,
|
| 888 |
+
400.000 - 499.999 kr.,4.0,1.0,55,1.0,1.0,123,3,0.5,1.0
|
| 889 |
+
200.000 - 299.999 kr.,4.0,0.0,41,,1.0,124,3,1.0,
|
| 890 |
+
600.000 - 699.999 kr.,5.0,1.0,35,,1.0,129,3,0.75,
|
| 891 |
+
200.000 - 299.999 kr.,4.0,1.0,37,1.0,1.0,131,3,0.75,1.0
|
| 892 |
+
400.000 - 499.999 kr.,2.0,1.0,51,0.0,1.0,132,3,0.5,0.0
|
| 893 |
+
100.000 - 199.999 kr.,3.0,0.0,48,,1.0,142,3,0.75,
|
| 894 |
+
400.000 - 499.999 kr.,1.0,1.0,62,,1.0,144,3,0.75,
|
| 895 |
+
100.000 - 199.999 kr.,1.0,1.0,64,0.0,1.0,145,3,0.5,0.0
|
| 896 |
+
500.000 - 599.999 kr.,4.0,1.0,66,,1.0,146,3,0.5,
|
| 897 |
+
200.000 - 299.999 kr.,4.0,0.0,33,,1.0,150,3,1.0,
|
| 898 |
+
100.000 - 199.999 kr.,4.0,1.0,21,1.0,1.0,151,3,0.25,1.0
|
| 899 |
+
400.000 - 499.999 kr.,5.0,1.0,61,1.0,1.0,174,3,0.75,1.0
|
| 900 |
+
300.000 - 399.999 kr.,2.0,0.0,51,1.0,1.0,175,3,0.75,1.0
|
| 901 |
+
700.000 eller derover,4.0,1.0,45,0.0,1.0,178,3,0.5,0.0
|
| 902 |
+
700.000 eller derover,4.0,1.0,63,0.0,1.0,180,3,0.5,0.0
|
| 903 |
+
400.000 - 499.999 kr.,2.0,0.0,46,1.0,1.0,184,3,0.25,1.0
|
| 904 |
+
600.000 - 699.999 kr.,4.0,1.0,34,,1.0,185,3,0.5,
|
| 905 |
+
300.000 - 399.999 kr.,4.0,1.0,28,1.0,1.0,191,3,0.5,1.0
|
| 906 |
+
700.000 eller derover,5.0,1.0,62,,1.0,199,3,0.5,
|
| 907 |
+
300.000 - 399.999 kr.,4.0,0.0,71,0.0,1.0,200,3,0.5,0.0
|
| 908 |
+
700.000 eller derover,5.0,1.0,49,0.0,1.0,202,3,1.0,0.0
|
| 909 |
+
300.000 - 399.999 kr.,4.0,0.0,56,,1.0,204,3,0.75,
|
| 910 |
+
500.000 - 599.999 kr.,4.0,0.0,53,,1.0,209,3,0.5,
|
| 911 |
+
700.000 eller derover,4.0,1.0,58,1.0,1.0,215,3,1.0,1.0
|
| 912 |
+
700.000 eller derover,4.0,1.0,37,,1.0,217,3,0.5,
|
| 913 |
+
Onsker ikke at oplyse,2.0,1.0,56,0.0,1.0,228,3,0.75,0.0
|
| 914 |
+
300.000 - 399.999 kr.,5.0,1.0,29,,1.0,234,3,1.0,
|
| 915 |
+
400.000 - 499.999 kr.,2.0,1.0,45,1.0,1.0,237,3,0.75,1.0
|
| 916 |
+
400.000 - 499.999 kr.,2.0,1.0,61,0.0,1.0,250,3,0.75,0.0
|
| 917 |
+
Onsker ikke at oplyse,4.0,1.0,67,,1.0,251,3,0.25,
|
| 918 |
+
700.000 eller derover,4.0,1.0,47,0.0,1.0,252,3,1.0,0.0
|
| 919 |
+
600.000 - 699.999 kr.,4.0,1.0,57,,1.0,257,3,0.75,
|
| 920 |
+
500.000 - 599.999 kr.,4.0,1.0,51,,1.0,262,3,1.0,
|
| 921 |
+
700.000 eller derover,5.0,0.0,30,1.0,1.0,264,3,0.75,1.0
|
| 922 |
+
700.000 eller derover,5.0,1.0,35,0.0,1.0,270,3,0.5,0.0
|
| 923 |
+
700.000 eller derover,2.0,1.0,55,0.0,1.0,271,3,0.5,0.0
|
| 924 |
+
500.000 - 599.999 kr.,2.0,0.0,43,,1.0,274,3,0.5,
|
| 925 |
+
600.000 - 699.999 kr.,5.0,0.0,40,1.0,1.0,283,3,0.5,1.0
|
| 926 |
+
700.000 eller derover,4.0,0.0,54,1.0,1.0,288,3,0.5,1.0
|
| 927 |
+
400.000 - 499.999 kr.,4.0,0.0,47,1.0,1.0,291,3,0.5,1.0
|
| 928 |
+
300.000 - 399.999 kr.,5.0,1.0,28,,1.0,293,3,1.0,
|
| 929 |
+
400.000 - 499.999 kr.,4.0,1.0,43,1.0,1.0,296,3,0.5,1.0
|
| 930 |
+
700.000 eller derover,2.0,0.0,45,,1.0,300,3,0.5,
|
| 931 |
+
300.000 - 399.999 kr.,4.0,0.0,34,1.0,1.0,304,3,1.0,1.0
|
| 932 |
+
600.000 - 699.999 kr.,5.0,1.0,69,1.0,1.0,306,3,0.75,1.0
|
| 933 |
+
500.000 - 599.999 kr.,4.0,0.0,42,1.0,1.0,307,3,0.75,1.0
|
| 934 |
+
400.000 - 499.999 kr.,2.0,1.0,47,1.0,1.0,308,3,0.5,1.0
|
| 935 |
+
Onsker ikke at oplyse,3.0,0.0,41,0.0,1.0,309,3,0.75,0.0
|
| 936 |
+
700.000 eller derover,1.0,0.0,58,0.0,1.0,311,3,0.75,0.0
|
| 937 |
+
600.000 - 699.999 kr.,4.0,0.0,51,1.0,1.0,314,3,0.5,1.0
|
| 938 |
+
300.000 - 399.999 kr.,2.0,1.0,64,,1.0,316,3,0.75,
|
| 939 |
+
100.000 - 199.999 kr.,5.0,1.0,51,,1.0,318,3,0.25,
|
| 940 |
+
500.000 - 599.999 kr.,3.0,1.0,43,0.0,1.0,323,3,0.75,0.0
|
| 941 |
+
400.000 - 499.999 kr.,2.0,1.0,60,,1.0,325,3,1.0,
|
| 942 |
+
200.000 - 299.999 kr.,1.0,1.0,49,,1.0,326,3,1.0,
|
| 943 |
+
200.000 - 299.999 kr.,4.0,0.0,55,0.0,1.0,327,3,0.75,0.0
|
| 944 |
+
600.000 - 699.999 kr.,4.0,0.0,55,,1.0,329,3,0.5,
|
| 945 |
+
700.000 eller derover,4.0,1.0,46,,1.0,344,3,0.5,
|
| 946 |
+
600.000 - 699.999 kr.,2.0,0.0,43,1.0,1.0,346,3,0.5,1.0
|
| 947 |
+
700.000 eller derover,5.0,1.0,49,,1.0,348,3,1.0,
|
| 948 |
+
700.000 eller derover,5.0,1.0,63,0.0,1.0,354,3,1.0,0.0
|
| 949 |
+
700.000 eller derover,3.0,1.0,51,1.0,1.0,358,3,0.25,1.0
|
| 950 |
+
500.000 - 599.999 kr.,2.0,1.0,50,1.0,1.0,366,3,1.0,1.0
|
| 951 |
+
700.000 eller derover,4.0,0.0,39,0.0,1.0,370,3,0.25,0.0
|
| 952 |
+
200.000 - 299.999 kr.,2.0,0.0,50,,1.0,374,3,1.0,
|
| 953 |
+
400.000 - 499.999 kr.,5.0,0.0,62,1.0,1.0,376,3,0.5,1.0
|
| 954 |
+
500.000 - 599.999 kr.,5.0,1.0,51,,1.0,379,3,0.25,
|
| 955 |
+
300.000 - 399.999 kr.,1.0,1.0,56,,1.0,381,3,0.5,
|
| 956 |
+
400.000 - 499.999 kr.,2.0,1.0,60,0.0,1.0,392,3,0.75,0.0
|
| 957 |
+
500.000 - 599.999 kr.,4.0,0.0,60,,1.0,393,3,0.75,
|
| 958 |
+
200.000 - 299.999 kr.,4.0,1.0,78,1.0,1.0,399,3,0.5,1.0
|
| 959 |
+
600.000 - 699.999 kr.,2.0,1.0,55,,1.0,407,3,0.75,
|
| 960 |
+
700.000 eller derover,5.0,0.0,64,1.0,1.0,413,3,0.5,1.0
|
| 961 |
+
600.000 - 699.999 kr.,2.0,1.0,52,0.0,1.0,428,3,0.5,0.0
|
| 962 |
+
500.000 - 599.999 kr.,2.0,1.0,59,1.0,1.0,431,3,0.75,1.0
|
| 963 |
+
700.000 eller derover,5.0,1.0,48,0.0,1.0,432,3,0.75,0.0
|
| 964 |
+
600.000 - 699.999 kr.,3.0,1.0,55,1.0,1.0,433,3,1.0,1.0
|
| 965 |
+
700.000 eller derover,5.0,1.0,38,1.0,1.0,436,3,0.75,1.0
|
| 966 |
+
700.000 eller derover,4.0,1.0,48,1.0,1.0,438,3,0.75,1.0
|
| 967 |
+
100.000 - 199.999 kr.,5.0,0.0,25,,1.0,441,3,0.75,
|
| 968 |
+
Indtil 99.999 kr.,4.0,1.0,36,0.0,1.0,444,3,0.5,0.0
|
| 969 |
+
700.000 eller derover,5.0,1.0,60,,1.0,446,3,0.5,
|
| 970 |
+
200.000 - 299.999 kr.,2.0,1.0,48,1.0,1.0,447,3,0.75,1.0
|
| 971 |
+
500.000 - 599.999 kr.,1.0,0.0,61,,1.0,453,3,1.0,
|
| 972 |
+
700.000 eller derover,4.0,1.0,36,0.0,1.0,461,3,0.5,0.0
|
| 973 |
+
200.000 - 299.999 kr.,4.0,0.0,60,,1.0,467,3,0.5,
|
| 974 |
+
500.000 - 599.999 kr.,1.0,0.0,55,,1.0,468,3,1.0,
|
| 975 |
+
300.000 - 399.999 kr.,4.0,1.0,53,1.0,1.0,470,3,0.5,1.0
|
| 976 |
+
600.000 - 699.999 kr.,5.0,0.0,34,0.0,1.0,473,3,0.5,0.0
|
| 977 |
+
300.000 - 399.999 kr.,4.0,0.0,48,0.0,1.0,474,3,0.5,0.0
|
| 978 |
+
600.000 - 699.999 kr.,2.0,0.0,32,0.0,1.0,478,3,0.25,0.0
|
| 979 |
+
500.000 - 599.999 kr.,4.0,0.0,51,0.0,1.0,480,3,0.5,0.0
|
| 980 |
+
400.000 - 499.999 kr.,4.0,1.0,68,1.0,1.0,483,3,0.75,1.0
|
| 981 |
+
600.000 - 699.999 kr.,3.0,1.0,29,0.0,1.0,485,3,0.5,0.0
|
| 982 |
+
700.000 eller derover,5.0,1.0,61,,1.0,488,3,0.25,
|
| 983 |
+
100.000 - 199.999 kr.,1.0,1.0,48,1.0,1.0,490,3,0.75,1.0
|
| 984 |
+
500.000 - 599.999 kr.,5.0,1.0,59,0.0,1.0,499,3,0.5,0.0
|
| 985 |
+
300.000 - 399.999 kr.,4.0,0.0,32,,1.0,503,3,0.25,
|
| 986 |
+
500.000 - 599.999 kr.,4.0,1.0,56,0.0,1.0,505,3,0.75,0.0
|
| 987 |
+
300.000 - 399.999 kr.,4.0,0.0,64,1.0,1.0,507,3,1.0,1.0
|
| 988 |
+
700.000 eller derover,5.0,1.0,34,0.0,1.0,509,3,0.25,0.0
|
| 989 |
+
500.000 - 599.999 kr.,4.0,1.0,50,,1.0,510,3,0.5,
|
| 990 |
+
300.000 - 399.999 kr.,1.0,1.0,64,,1.0,515,3,0.5,
|
| 991 |
+
300.000 - 399.999 kr.,4.0,0.0,51,,1.0,517,3,1.0,
|
| 992 |
+
700.000 eller derover,4.0,1.0,46,0.0,1.0,528,3,0.75,0.0
|
| 993 |
+
Onsker ikke at oplyse,4.0,1.0,67,0.0,1.0,529,3,0.5,0.0
|
| 994 |
+
700.000 eller derover,5.0,1.0,61,1.0,1.0,532,3,0.75,1.0
|
| 995 |
+
300.000 - 399.999 kr.,2.0,1.0,56,1.0,1.0,533,3,0.5,1.0
|
| 996 |
+
400.000 - 499.999 kr.,1.0,0.0,21,1.0,1.0,537,3,0.5,1.0
|
| 997 |
+
400.000 - 499.999 kr.,2.0,0.0,60,,1.0,539,3,0.75,
|
| 998 |
+
400.000 - 499.999 kr.,4.0,1.0,64,1.0,1.0,544,3,0.5,1.0
|
| 999 |
+
600.000 - 699.999 kr.,2.0,0.0,54,,1.0,545,3,0.5,
|
| 1000 |
+
500.000 - 599.999 kr.,1.0,1.0,48,1.0,1.0,546,3,0.75,1.0
|
| 1001 |
+
300.000 - 399.999 kr.,2.0,1.0,59,1.0,1.0,551,3,0.75,1.0
|
| 1002 |
+
600.000 - 699.999 kr.,4.0,1.0,51,1.0,1.0,553,3,0.25,1.0
|
| 1003 |
+
700.000 eller derover,2.0,1.0,50,0.0,1.0,554,3,0.25,0.0
|
| 1004 |
+
500.000 - 599.999 kr.,4.0,1.0,46,,1.0,558,3,0.5,
|
| 1005 |
+
100.000 - 199.999 kr.,4.0,1.0,41,,1.0,560,3,0.25,
|
| 1006 |
+
700.000 eller derover,4.0,1.0,54,0.0,1.0,561,3,0.5,0.0
|
| 1007 |
+
Onsker ikke at oplyse,2.0,1.0,45,,1.0,562,3,0.25,
|
| 1008 |
+
600.000 - 699.999 kr.,4.0,1.0,57,,1.0,565,3,0.25,
|
| 1009 |
+
Onsker ikke at oplyse,4.0,1.0,62,,1.0,567,3,0.75,
|
| 1010 |
+
Onsker ikke at oplyse,2.0,0.0,43,0.0,1.0,575,3,0.5,0.0
|
| 1011 |
+
Onsker ikke at oplyse,2.0,0.0,54,1.0,1.0,576,3,1.0,1.0
|
| 1012 |
+
Onsker ikke at oplyse,4.0,1.0,70,0.0,1.0,577,3,0.5,0.0
|
| 1013 |
+
600.000 - 699.999 kr.,4.0,0.0,52,1.0,1.0,580,3,0.75,1.0
|
| 1014 |
+
500.000 - 599.999 kr.,5.0,0.0,55,1.0,1.0,584,3,0.5,1.0
|
| 1015 |
+
700.000 eller derover,4.0,0.0,50,0.0,1.0,587,3,0.75,0.0
|
| 1016 |
+
100.000 - 199.999 kr.,3.0,0.0,25,1.0,1.0,592,3,0.5,1.0
|
| 1017 |
+
500.000 - 599.999 kr.,2.0,0.0,39,1.0,1.0,602,3,0.5,1.0
|
| 1018 |
+
700.000 eller derover,4.0,0.0,57,1.0,1.0,608,3,0.75,1.0
|
| 1019 |
+
500.000 - 599.999 kr.,2.0,1.0,37,0.0,1.0,609,3,0.75,0.0
|
| 1020 |
+
300.000 - 399.999 kr.,1.0,1.0,65,0.0,1.0,611,3,0.75,0.0
|
| 1021 |
+
Onsker ikke at oplyse,4.0,1.0,66,1.0,1.0,612,3,0.25,1.0
|
| 1022 |
+
700.000 eller derover,4.0,0.0,41,0.0,1.0,615,3,0.25,0.0
|
| 1023 |
+
500.000 - 599.999 kr.,4.0,1.0,43,0.0,1.0,617,3,0.75,0.0
|
| 1024 |
+
300.000 - 399.999 kr.,2.0,0.0,44,1.0,1.0,620,3,0.5,1.0
|
| 1025 |
+
300.000 - 399.999 kr.,2.0,1.0,44,0.0,1.0,622,3,0.5,0.0
|
| 1026 |
+
Onsker ikke at oplyse,5.0,1.0,54,1.0,1.0,623,3,0.25,1.0
|
| 1027 |
+
Onsker ikke at oplyse,4.0,1.0,61,0.0,1.0,625,3,0.5,0.0
|
| 1028 |
+
500.000 - 599.999 kr.,2.0,0.0,53,0.0,1.0,626,3,0.5,0.0
|
| 1029 |
+
400.000 - 499.999 kr.,4.0,0.0,61,1.0,1.0,627,3,0.5,1.0
|
| 1030 |
+
300.000 - 399.999 kr.,5.0,0.0,29,,1.0,630,3,0.5,
|
| 1031 |
+
Onsker ikke at oplyse,4.0,0.0,48,0.0,1.0,634,3,0.5,0.0
|
| 1032 |
+
700.000 eller derover,5.0,0.0,43,,1.0,637,3,0.75,
|
| 1033 |
+
700.000 eller derover,4.0,1.0,47,0.0,1.0,641,3,1.0,0.0
|
| 1034 |
+
100.000 - 199.999 kr.,4.0,0.0,55,,1.0,647,3,0.5,
|
| 1035 |
+
100.000 - 199.999 kr.,5.0,1.0,58,,1.0,648,3,0.5,
|
| 1036 |
+
300.000 - 399.999 kr.,3.0,1.0,48,,1.0,649,3,0.75,
|
| 1037 |
+
300.000 - 399.999 kr.,3.0,1.0,51,1.0,1.0,655,3,0.5,1.0
|
| 1038 |
+
Indtil 99.999 kr.,4.0,0.0,44,1.0,1.0,657,3,0.5,1.0
|
| 1039 |
+
200.000 - 299.999 kr.,2.0,1.0,64,,1.0,659,3,0.5,
|
| 1040 |
+
700.000 eller derover,4.0,1.0,56,,1.0,660,3,0.5,
|
| 1041 |
+
500.000 - 599.999 kr.,3.0,0.0,43,0.0,1.0,661,3,0.75,0.0
|
| 1042 |
+
500.000 - 599.999 kr.,3.0,0.0,27,,1.0,665,3,0.75,
|
| 1043 |
+
500.000 - 599.999 kr.,5.0,1.0,71,,1.0,666,3,0.5,
|
| 1044 |
+
600.000 - 699.999 kr.,2.0,0.0,36,0.0,1.0,679,3,1.0,0.0
|
| 1045 |
+
100.000 - 199.999 kr.,4.0,0.0,25,1.0,1.0,692,3,0.5,1.0
|
| 1046 |
+
600.000 - 699.999 kr.,4.0,0.0,48,1.0,1.0,696,3,0.5,1.0
|
| 1047 |
+
200.000 - 299.999 kr.,4.0,1.0,47,,1.0,697,3,0.75,
|
| 1048 |
+
600.000 - 699.999 kr.,5.0,1.0,63,1.0,1.0,702,3,0.75,1.0
|
| 1049 |
+
300.000 - 399.999 kr.,2.0,1.0,67,,1.0,705,3,0.5,
|
| 1050 |
+
300.000 - 399.999 kr.,4.0,1.0,67,0.0,1.0,708,3,0.5,0.0
|
| 1051 |
+
700.000 eller derover,5.0,0.0,51,0.0,1.0,710,3,1.0,0.0
|
| 1052 |
+
300.000 - 399.999 kr.,2.0,1.0,66,1.0,1.0,711,3,0.5,1.0
|
| 1053 |
+
300.000 - 399.999 kr.,3.0,0.0,48,,1.0,713,3,0.25,
|
| 1054 |
+
Onsker ikke at oplyse,4.0,1.0,57,0.0,1.0,714,3,0.75,0.0
|
| 1055 |
+
200.000 - 299.999 kr.,4.0,0.0,61,1.0,1.0,717,3,1.0,1.0
|
| 1056 |
+
300.000 - 399.999 kr.,4.0,1.0,69,0.0,1.0,718,3,0.75,0.0
|
| 1057 |
+
700.000 eller derover,2.0,1.0,53,0.0,1.0,720,3,0.75,0.0
|
| 1058 |
+
700.000 eller derover,2.0,1.0,61,0.0,1.0,721,3,0.25,0.0
|
| 1059 |
+
300.000 - 399.999 kr.,4.0,1.0,46,1.0,1.0,723,3,0.75,1.0
|
| 1060 |
+
500.000 - 599.999 kr.,4.0,1.0,64,0.0,1.0,726,3,0.5,0.0
|
| 1061 |
+
700.000 eller derover,4.0,1.0,58,,1.0,728,3,0.5,
|
| 1062 |
+
600.000 - 699.999 kr.,4.0,0.0,47,1.0,1.0,730,3,0.5,1.0
|
| 1063 |
+
100.000 - 199.999 kr.,3.0,1.0,25,,1.0,732,3,1.0,
|
| 1064 |
+
600.000 - 699.999 kr.,1.0,0.0,38,0.0,1.0,744,3,1.0,0.0
|
| 1065 |
+
700.000 eller derover,4.0,0.0,53,1.0,1.0,747,3,1.0,1.0
|
| 1066 |
+
200.000 - 299.999 kr.,4.0,0.0,34,1.0,1.0,751,3,0.75,1.0
|
| 1067 |
+
100.000 - 199.999 kr.,2.0,1.0,29,,1.0,761,3,0.75,
|
| 1068 |
+
700.000 eller derover,4.0,1.0,60,1.0,1.0,763,3,0.75,1.0
|
| 1069 |
+
600.000 - 699.999 kr.,4.0,1.0,60,,1.0,772,3,0.5,
|
| 1070 |
+
300.000 - 399.999 kr.,2.0,1.0,64,,1.0,775,3,0.5,
|
| 1071 |
+
Onsker ikke at oplyse,4.0,1.0,58,0.0,1.0,781,3,0.5,0.0
|
| 1072 |
+
200.000 - 299.999 kr.,2.0,1.0,36,,1.0,784,3,0.5,
|
| 1073 |
+
700.000 eller derover,4.0,0.0,43,1.0,1.0,806,3,1.0,1.0
|
| 1074 |
+
100.000 - 199.999 kr.,4.0,1.0,33,,1.0,807,3,1.0,
|
| 1075 |
+
200.000 - 299.999 kr.,2.0,1.0,43,1.0,1.0,809,3,0.25,1.0
|
| 1076 |
+
600.000 - 699.999 kr.,4.0,1.0,50,,1.0,810,3,0.5,
|
| 1077 |
+
Onsker ikke at oplyse,4.0,0.0,62,,1.0,811,3,0.75,
|
| 1078 |
+
300.000 - 399.999 kr.,4.0,1.0,50,1.0,1.0,812,3,0.75,1.0
|
| 1079 |
+
700.000 eller derover,2.0,1.0,56,0.0,1.0,813,3,0.5,0.0
|
| 1080 |
+
700.000 eller derover,4.0,1.0,43,0.0,1.0,821,3,0.75,0.0
|
| 1081 |
+
Onsker ikke at oplyse,3.0,1.0,40,,1.0,822,3,1.0,
|
| 1082 |
+
Onsker ikke at oplyse,1.0,1.0,60,,1.0,823,3,0.75,
|
| 1083 |
+
500.000 - 599.999 kr.,4.0,1.0,56,1.0,1.0,824,3,0.5,1.0
|
| 1084 |
+
700.000 eller derover,4.0,1.0,39,,1.0,825,3,0.75,
|
| 1085 |
+
300.000 - 399.999 kr.,4.0,1.0,41,,1.0,834,3,0.25,
|
| 1086 |
+
700.000 eller derover,5.0,1.0,55,,1.0,837,3,0.5,
|
| 1087 |
+
300.000 - 399.999 kr.,5.0,1.0,33,,1.0,839,3,0.75,
|
| 1088 |
+
700.000 eller derover,2.0,0.0,41,0.0,1.0,841,3,0.75,0.0
|
| 1089 |
+
700.000 eller derover,4.0,1.0,55,,1.0,844,3,0.5,
|
| 1090 |
+
700.000 eller derover,2.0,1.0,46,1.0,1.0,850,3,0.5,1.0
|
| 1091 |
+
300.000 - 399.999 kr.,4.0,0.0,43,1.0,1.0,853,3,0.5,1.0
|
| 1092 |
+
600.000 - 699.999 kr.,4.0,0.0,45,,1.0,854,3,0.5,
|
| 1093 |
+
400.000 - 499.999 kr.,4.0,1.0,73,,1.0,859,3,0.75,
|
| 1094 |
+
300.000 - 399.999 kr.,1.0,0.0,57,1.0,1.0,863,3,0.75,1.0
|
| 1095 |
+
700.000 eller derover,2.0,0.0,48,,1.0,865,3,1.0,
|
| 1096 |
+
200.000 - 299.999 kr.,3.0,1.0,58,1.0,1.0,866,3,0.75,1.0
|
| 1097 |
+
100.000 - 199.999 kr.,5.0,1.0,55,,1.0,878,3,0.75,
|
| 1098 |
+
600.000 - 699.999 kr.,2.0,1.0,54,0.0,1.0,881,3,0.5,0.0
|
| 1099 |
+
400.000 - 499.999 kr.,2.0,1.0,58,,1.0,884,3,0.5,
|
| 1100 |
+
Indtil 99.999 kr.,3.0,1.0,19,1.0,1.0,886,3,0.5,1.0
|
| 1101 |
+
300.000 - 399.999 kr.,5.0,1.0,62,0.0,1.0,889,3,0.5,0.0
|
| 1102 |
+
200.000 - 299.999 kr.,4.0,0.0,40,,1.0,893,3,0.5,
|
| 1103 |
+
600.000 - 699.999 kr.,2.0,1.0,59,1.0,1.0,895,3,0.75,1.0
|
| 1104 |
+
700.000 eller derover,5.0,1.0,57,0.0,1.0,897,3,0.5,0.0
|
| 1105 |
+
Onsker ikke at oplyse,2.0,1.0,29,0.0,1.0,899,3,0.25,0.0
|
| 1106 |
+
400.000 - 499.999 kr.,1.0,1.0,44,1.0,1.0,900,3,0.75,1.0
|
| 1107 |
+
700.000 eller derover,5.0,1.0,37,0.0,1.0,902,3,0.75,0.0
|
| 1108 |
+
200.000 - 299.999 kr.,2.0,0.0,39,,1.0,907,3,0.5,
|
| 1109 |
+
100.000 - 199.999 kr.,3.0,0.0,38,0.0,1.0,908,3,0.5,0.0
|
| 1110 |
+
500.000 - 599.999 kr.,4.0,1.0,48,1.0,1.0,911,3,0.75,1.0
|
| 1111 |
+
500.000 - 599.999 kr.,4.0,0.0,60,0.0,1.0,915,3,0.5,0.0
|
| 1112 |
+
Onsker ikke at oplyse,5.0,1.0,48,0.0,1.0,920,3,0.75,0.0
|
| 1113 |
+
400.000 - 499.999 kr.,4.0,1.0,33,1.0,1.0,923,3,0.25,1.0
|
| 1114 |
+
400.000 - 499.999 kr.,5.0,0.0,33,1.0,1.0,924,3,0.5,1.0
|
| 1115 |
+
600.000 - 699.999 kr.,2.0,0.0,42,1.0,1.0,926,3,0.5,1.0
|
| 1116 |
+
700.000 eller derover,4.0,1.0,55,1.0,1.0,931,3,0.75,1.0
|
| 1117 |
+
400.000 - 499.999 kr.,5.0,1.0,70,0.0,1.0,936,3,1.0,0.0
|
| 1118 |
+
700.000 eller derover,5.0,0.0,40,,1.0,937,3,0.75,
|
| 1119 |
+
400.000 - 499.999 kr.,5.0,1.0,58,0.0,1.0,950,3,0.75,0.0
|
| 1120 |
+
700.000 eller derover,4.0,1.0,43,0.0,1.0,958,3,0.5,0.0
|
| 1121 |
+
600.000 - 699.999 kr.,5.0,0.0,29,1.0,1.0,962,3,0.75,1.0
|
| 1122 |
+
600.000 - 699.999 kr.,4.0,1.0,49,0.0,1.0,964,3,0.5,0.0
|
| 1123 |
+
700.000 eller derover,5.0,1.0,41,0.0,1.0,966,3,1.0,0.0
|
| 1124 |
+
200.000 - 299.999 kr.,4.0,0.0,47,,1.0,969,3,0.5,
|
| 1125 |
+
700.000 eller derover,2.0,1.0,50,0.0,1.0,973,3,0.75,0.0
|
| 1126 |
+
Indtil 99.999 kr.,3.0,0.0,25,1.0,1.0,975,3,0.75,1.0
|
| 1127 |
+
500.000 - 599.999 kr.,5.0,1.0,59,,1.0,980,3,0.75,
|
| 1128 |
+
700.000 eller derover,2.0,1.0,46,0.0,1.0,981,3,0.75,0.0
|
| 1129 |
+
700.000 eller derover,4.0,0.0,27,0.0,1.0,982,3,0.25,0.0
|
| 1130 |
+
300.000 - 399.999 kr.,5.0,1.0,59,1.0,1.0,984,3,0.5,1.0
|
| 1131 |
+
700.000 eller derover,1.0,1.0,53,1.0,1.0,987,3,0.75,1.0
|
| 1132 |
+
700.000 eller derover,5.0,0.0,37,1.0,1.0,988,3,0.75,1.0
|
| 1133 |
+
Onsker ikke at oplyse,5.0,0.0,57,,1.0,992,3,1.0,
|
| 1134 |
+
700.000 eller derover,5.0,0.0,38,1.0,1.0,993,3,0.5,1.0
|
| 1135 |
+
700.000 eller derover,5.0,1.0,57,,1.0,994,3,0.75,
|
| 1136 |
+
Onsker ikke at oplyse,5.0,1.0,56,1.0,1.0,997,3,0.25,1.0
|
| 1137 |
+
700.000 eller derover,4.0,0.0,59,1.0,1.0,999,3,1.0,1.0
|
| 1138 |
+
600.000 - 699.999 kr.,4.0,1.0,57,1.0,1.0,1001,3,0.25,1.0
|
| 1139 |
+
700.000 eller derover,5.0,0.0,53,1.0,1.0,1002,3,0.5,1.0
|
| 1140 |
+
700.000 eller derover,4.0,1.0,40,0.0,1.0,1004,3,0.75,0.0
|
| 1141 |
+
700.000 eller derover,4.0,1.0,57,0.0,1.0,1006,3,0.5,0.0
|
| 1142 |
+
600.000 - 699.999 kr.,5.0,1.0,52,1.0,1.0,1008,3,0.75,1.0
|
| 1143 |
+
500.000 - 599.999 kr.,4.0,1.0,53,,1.0,1011,3,0.5,
|
| 1144 |
+
500.000 - 599.999 kr.,5.0,0.0,63,,1.0,1012,3,0.5,
|
| 1145 |
+
400.000 - 499.999 kr.,4.0,0.0,46,1.0,1.0,1013,3,1.0,1.0
|
| 1146 |
+
400.000 - 499.999 kr.,2.0,0.0,43,1.0,1.0,1016,3,0.75,1.0
|
| 1147 |
+
100.000 - 199.999 kr.,4.0,1.0,33,,1.0,1017,3,1.0,
|
| 1148 |
+
600.000 - 699.999 kr.,5.0,0.0,67,,1.0,1020,3,0.75,
|
| 1149 |
+
500.000 - 599.999 kr.,1.0,1.0,57,1.0,1.0,1027,3,0.5,1.0
|
| 1150 |
+
700.000 eller derover,5.0,1.0,38,,1.0,1029,3,0.75,
|
| 1151 |
+
700.000 eller derover,4.0,1.0,41,0.0,1.0,1031,3,0.5,0.0
|
| 1152 |
+
700.000 eller derover,5.0,1.0,46,,1.0,1035,3,0.25,
|
| 1153 |
+
200.000 - 299.999 kr.,2.0,0.0,46,,1.0,1036,3,0.75,
|
| 1154 |
+
500.000 - 599.999 kr.,2.0,1.0,51,1.0,1.0,1039,3,0.5,1.0
|
| 1155 |
+
700.000 eller derover,3.0,1.0,45,0.0,1.0,1041,3,0.5,0.0
|
| 1156 |
+
700.000 eller derover,5.0,1.0,42,1.0,1.0,1043,3,0.75,1.0
|
| 1157 |
+
500.000 - 599.999 kr.,2.0,0.0,49,0.0,1.0,1044,3,0.75,0.0
|
| 1158 |
+
700.000 eller derover,4.0,1.0,58,,1.0,1047,3,1.0,
|
| 1159 |
+
400.000 - 499.999 kr.,2.0,0.0,58,0.0,1.0,1048,3,0.25,0.0
|
| 1160 |
+
100.000 - 199.999 kr.,3.0,1.0,24,0.0,1.0,1059,3,0.5,0.0
|
| 1161 |
+
700.000 eller derover,5.0,1.0,33,,1.0,1061,3,0.75,
|
| 1162 |
+
Onsker ikke at oplyse,5.0,0.0,43,0.0,1.0,1066,3,0.75,0.0
|
| 1163 |
+
Onsker ikke at oplyse,2.0,1.0,53,1.0,1.0,1070,3,1.0,1.0
|
| 1164 |
+
600.000 - 699.999 kr.,2.0,0.0,52,,1.0,1076,3,0.75,
|
| 1165 |
+
700.000 eller derover,5.0,1.0,32,0.0,1.0,1078,3,0.25,0.0
|
| 1166 |
+
400.000 - 499.999 kr.,2.0,0.0,59,1.0,1.0,1079,3,0.75,1.0
|
| 1167 |
+
500.000 - 599.999 kr.,4.0,0.0,56,1.0,1.0,1085,3,1.0,1.0
|
| 1168 |
+
600.000 - 699.999 kr.,3.0,1.0,55,,1.0,1086,3,0.25,
|
| 1169 |
+
600.000 - 699.999 kr.,3.0,1.0,55,,1.0,1087,3,0.25,
|
| 1170 |
+
500.000 - 599.999 kr.,5.0,0.0,30,,1.0,1095,3,0.25,
|
| 1171 |
+
300.000 - 399.999 kr.,2.0,1.0,42,,1.0,1102,3,0.5,
|
| 1172 |
+
700.000 eller derover,4.0,0.0,47,0.0,1.0,1103,3,0.75,0.0
|
| 1173 |
+
600.000 - 699.999 kr.,4.0,1.0,41,1.0,1.0,1109,3,0.5,1.0
|
| 1174 |
+
500.000 - 599.999 kr.,4.0,1.0,33,1.0,1.0,1114,3,0.75,1.0
|
| 1175 |
+
600.000 - 699.999 kr.,1.0,0.0,6,,1.0,1115,3,0.5,
|
| 1176 |
+
300.000 - 399.999 kr.,2.0,0.0,60,1.0,1.0,1120,3,0.75,1.0
|
| 1177 |
+
300.000 - 399.999 kr.,4.0,0.0,49,1.0,1.0,1121,3,0.5,1.0
|
| 1178 |
+
500.000 - 599.999 kr.,2.0,1.0,61,0.0,1.0,1122,3,1.0,0.0
|
| 1179 |
+
Onsker ikke at oplyse,5.0,1.0,54,0.0,1.0,1125,3,0.75,0.0
|
| 1180 |
+
600.000 - 699.999 kr.,2.0,1.0,51,,1.0,1129,3,0.25,
|
| 1181 |
+
500.000 - 599.999 kr.,2.0,1.0,58,0.0,1.0,1130,3,0.75,0.0
|
| 1182 |
+
700.000 eller derover,4.0,0.0,53,1.0,1.0,1131,3,0.5,1.0
|
| 1183 |
+
500.000 - 599.999 kr.,4.0,0.0,63,,1.0,1132,3,0.75,
|
| 1184 |
+
300.000 - 399.999 kr.,4.0,1.0,54,1.0,1.0,1135,3,0.75,1.0
|
| 1185 |
+
700.000 eller derover,5.0,1.0,64,1.0,1.0,1137,3,0.75,1.0
|
| 1186 |
+
600.000 - 699.999 kr.,4.0,1.0,46,0.0,1.0,1139,3,0.75,0.0
|
| 1187 |
+
700.000 eller derover,2.0,0.0,52,1.0,1.0,1141,3,0.5,1.0
|
| 1188 |
+
500.000 - 599.999 kr.,4.0,1.0,52,,1.0,1142,3,1.0,
|
| 1189 |
+
Onsker ikke at oplyse,3.0,0.0,51,,1.0,1145,3,1.0,
|
| 1190 |
+
500.000 - 599.999 kr.,4.0,1.0,59,,1.0,1150,3,0.5,
|
| 1191 |
+
200.000 - 299.999 kr.,1.0,1.0,65,1.0,1.0,1162,3,1.0,1.0
|
| 1192 |
+
700.000 eller derover,4.0,0.0,40,,1.0,1164,3,0.5,
|
| 1193 |
+
700.000 eller derover,2.0,1.0,53,,1.0,1171,3,0.25,
|
| 1194 |
+
400.000 - 499.999 kr.,5.0,1.0,44,0.0,1.0,1173,3,1.0,0.0
|
| 1195 |
+
100.000 - 199.999 kr.,2.0,1.0,78,1.0,1.0,1174,3,0.5,1.0
|
| 1196 |
+
700.000 eller derover,5.0,1.0,29,0.0,1.0,1182,3,0.5,0.0
|
| 1197 |
+
700.000 eller derover,2.0,1.0,32,0.0,1.0,1187,3,0.25,0.0
|
| 1198 |
+
600.000 - 699.999 kr.,4.0,1.0,26,1.0,1.0,1195,3,0.75,1.0
|
| 1199 |
+
100.000 - 199.999 kr.,1.0,1.0,30,1.0,1.0,1197,3,0.75,1.0
|
| 1200 |
+
200.000 - 299.999 kr.,2.0,1.0,59,0.0,1.0,1199,3,0.5,0.0
|
| 1201 |
+
500.000 - 599.999 kr.,1.0,1.0,58,1.0,1.0,1200,3,0.75,1.0
|
| 1202 |
+
500.000 - 599.999 kr.,2.0,0.0,49,,1.0,1203,3,0.25,
|
| 1203 |
+
300.000 - 399.999 kr.,3.0,1.0,42,1.0,1.0,1206,3,1.0,1.0
|
| 1204 |
+
100.000 - 199.999 kr.,4.0,0.0,63,,1.0,1211,3,0.25,
|
| 1205 |
+
300.000 - 399.999 kr.,2.0,0.0,45,,1.0,1213,3,0.75,
|
| 1206 |
+
500.000 - 599.999 kr.,4.0,0.0,41,,1.0,1219,3,0.5,
|
| 1207 |
+
500.000 - 599.999 kr.,2.0,1.0,35,1.0,1.0,1224,3,0.5,1.0
|
| 1208 |
+
Onsker ikke at oplyse,4.0,1.0,43,1.0,1.0,1230,3,1.0,1.0
|
| 1209 |
+
300.000 - 399.999 kr.,2.0,1.0,35,0.0,1.0,1235,3,0.5,0.0
|
| 1210 |
+
300.000 - 399.999 kr.,5.0,0.0,44,1.0,1.0,1237,3,1.0,1.0
|
| 1211 |
+
300.000 - 399.999 kr.,4.0,0.0,52,,1.0,1245,3,1.0,
|
| 1212 |
+
400.000 - 499.999 kr.,3.0,1.0,58,,1.0,1247,3,0.75,
|
| 1213 |
+
100.000 - 199.999 kr.,4.0,0.0,46,,1.0,1248,3,0.5,
|
| 1214 |
+
500.000 - 599.999 kr.,2.0,1.0,40,1.0,1.0,1250,3,1.0,1.0
|
| 1215 |
+
100.000 - 199.999 kr.,2.0,1.0,43,,1.0,1252,3,1.0,
|
| 1216 |
+
100.000 - 199.999 kr.,4.0,0.0,54,1.0,1.0,1259,3,0.5,1.0
|
| 1217 |
+
500.000 - 599.999 kr.,4.0,1.0,60,,1.0,1261,3,0.75,
|
| 1218 |
+
700.000 eller derover,4.0,1.0,52,0.0,1.0,1263,3,0.75,0.0
|
| 1219 |
+
600.000 - 699.999 kr.,1.0,1.0,59,1.0,1.0,1264,3,0.5,1.0
|
| 1220 |
+
200.000 - 299.999 kr.,4.0,1.0,62,,1.0,1267,3,1.0,
|
| 1221 |
+
Onsker ikke at oplyse,3.0,1.0,19,,1.0,1275,3,0.0,
|
| 1222 |
+
600.000 - 699.999 kr.,3.0,0.0,51,1.0,1.0,1277,3,0.75,1.0
|
| 1223 |
+
100.000 - 199.999 kr.,4.0,0.0,53,1.0,1.0,1281,3,1.0,1.0
|
| 1224 |
+
300.000 - 399.999 kr.,5.0,0.0,28,,1.0,1282,3,0.5,
|
| 1225 |
+
600.000 - 699.999 kr.,2.0,0.0,50,1.0,1.0,1286,3,0.75,1.0
|
| 1226 |
+
300.000 - 399.999 kr.,1.0,0.0,58,1.0,1.0,1294,3,0.5,1.0
|
| 1227 |
+
600.000 - 699.999 kr.,4.0,1.0,56,1.0,1.0,1295,3,0.5,1.0
|
| 1228 |
+
600.000 - 699.999 kr.,5.0,1.0,44,1.0,1.0,1296,3,0.5,1.0
|
| 1229 |
+
Onsker ikke at oplyse,4.0,0.0,55,,1.0,1297,3,0.5,
|
| 1230 |
+
300.000 - 399.999 kr.,3.0,0.0,23,,1.0,1304,3,0.5,
|
| 1231 |
+
300.000 - 399.999 kr.,4.0,1.0,62,1.0,1.0,1307,3,0.75,1.0
|
| 1232 |
+
700.000 eller derover,4.0,0.0,48,,1.0,1308,3,0.5,
|
| 1233 |
+
700.000 eller derover,4.0,1.0,30,,1.0,1313,3,0.5,
|
| 1234 |
+
600.000 - 699.999 kr.,5.0,0.0,28,0.0,1.0,1314,3,0.75,0.0
|
| 1235 |
+
700.000 eller derover,5.0,1.0,53,1.0,1.0,1315,3,1.0,1.0
|
| 1236 |
+
100.000 - 199.999 kr.,1.0,1.0,65,0.0,1.0,1320,3,0.75,0.0
|
| 1237 |
+
200.000 - 299.999 kr.,2.0,1.0,65,,1.0,1323,3,0.5,
|
| 1238 |
+
500.000 - 599.999 kr.,2.0,0.0,45,0.0,1.0,1324,3,0.75,0.0
|
| 1239 |
+
200.000 - 299.999 kr.,4.0,0.0,67,1.0,1.0,1325,3,0.75,1.0
|
| 1240 |
+
500.000 - 599.999 kr.,4.0,0.0,53,,1.0,1330,3,0.75,
|
| 1241 |
+
Onsker ikke at oplyse,2.0,0.0,55,,1.0,1339,3,0.75,
|
| 1242 |
+
400.000 - 499.999 kr.,2.0,1.0,37,0.0,1.0,1344,3,0.25,0.0
|
| 1243 |
+
700.000 eller derover,5.0,1.0,50,0.0,1.0,1346,3,0.25,0.0
|
| 1244 |
+
400.000 - 499.999 kr.,4.0,1.0,85,0.0,1.0,1347,3,1.0,0.0
|
| 1245 |
+
400.000 - 499.999 kr.,4.0,0.0,58,0.0,1.0,1348,3,0.75,0.0
|
| 1246 |
+
500.000 - 599.999 kr.,4.0,0.0,56,0.0,1.0,1353,3,0.75,0.0
|
| 1247 |
+
400.000 - 499.999 kr.,2.0,1.0,51,1.0,1.0,1355,3,0.75,1.0
|
| 1248 |
+
300.000 - 399.999 kr.,2.0,0.0,64,,1.0,1358,3,0.5,
|
| 1249 |
+
600.000 - 699.999 kr.,2.0,1.0,52,,1.0,1361,3,0.5,
|
| 1250 |
+
700.000 eller derover,5.0,1.0,49,,1.0,1362,3,0.75,
|
| 1251 |
+
700.000 eller derover,4.0,1.0,36,0.0,1.0,1367,3,0.5,0.0
|
| 1252 |
+
700.000 eller derover,3.0,1.0,55,0.0,1.0,1369,3,0.5,0.0
|
| 1253 |
+
300.000 - 399.999 kr.,5.0,1.0,61,0.0,1.0,1370,3,0.75,0.0
|
| 1254 |
+
700.000 eller derover,5.0,0.0,42,1.0,1.0,1371,3,1.0,1.0
|
| 1255 |
+
300.000 - 399.999 kr.,5.0,0.0,38,1.0,1.0,1373,3,0.25,1.0
|
| 1256 |
+
400.000 - 499.999 kr.,4.0,1.0,63,1.0,1.0,1375,3,0.75,1.0
|
| 1257 |
+
400.000 - 499.999 kr.,5.0,1.0,66,,1.0,1380,3,0.25,
|
| 1258 |
+
Indtil 99.999 kr.,3.0,0.0,21,1.0,1.0,1384,3,0.5,1.0
|
| 1259 |
+
100.000 - 199.999 kr.,3.0,0.0,21,1.0,1.0,1385,3,0.25,1.0
|
| 1260 |
+
200.000 - 299.999 kr.,5.0,1.0,28,,1.0,1390,3,0.5,
|
| 1261 |
+
600.000 - 699.999 kr.,4.0,0.0,53,1.0,1.0,1392,3,0.5,1.0
|
| 1262 |
+
300.000 - 399.999 kr.,4.0,1.0,26,0.0,1.0,1393,3,0.0,0.0
|
| 1263 |
+
200.000 - 299.999 kr.,2.0,1.0,64,0.0,1.0,1396,3,0.75,0.0
|
| 1264 |
+
400.000 - 499.999 kr.,1.0,1.0,57,1.0,1.0,1399,3,0.25,1.0
|
| 1265 |
+
700.000 eller derover,5.0,1.0,33,0.0,1.0,1402,3,0.75,0.0
|
| 1266 |
+
600.000 - 699.999 kr.,4.0,0.0,42,1.0,1.0,1403,3,0.25,1.0
|
| 1267 |
+
300.000 - 399.999 kr.,4.0,1.0,48,0.0,1.0,1406,3,0.5,0.0
|
| 1268 |
+
Onsker ikke at oplyse,3.0,0.0,19,1.0,1.0,1409,3,0.75,1.0
|
| 1269 |
+
600.000 - 699.999 kr.,4.0,1.0,42,1.0,1.0,1412,3,0.75,1.0
|
| 1270 |
+
600.000 - 699.999 kr.,4.0,0.0,53,1.0,1.0,1413,3,0.5,1.0
|
| 1271 |
+
700.000 eller derover,4.0,1.0,58,1.0,1.0,1416,3,0.75,1.0
|
| 1272 |
+
100.000 - 199.999 kr.,4.0,1.0,43,1.0,1.0,1417,3,1.0,1.0
|
| 1273 |
+
Onsker ikke at oplyse,2.0,0.0,55,0.0,1.0,1422,3,0.5,0.0
|
| 1274 |
+
600.000 - 699.999 kr.,3.0,0.0,46,1.0,1.0,1426,3,0.75,1.0
|
| 1275 |
+
700.000 eller derover,4.0,1.0,57,1.0,1.0,1431,3,0.75,1.0
|
| 1276 |
+
500.000 - 599.999 kr.,4.0,0.0,57,1.0,1.0,1433,3,0.5,1.0
|
| 1277 |
+
300.000 - 399.999 kr.,4.0,1.0,97,0.0,1.0,1437,3,0.25,0.0
|
| 1278 |
+
700.000 eller derover,2.0,1.0,49,,1.0,1439,3,0.75,
|
| 1279 |
+
Indtil 99.999 kr.,5.0,1.0,25,0.0,1.0,1440,3,1.0,0.0
|
| 1280 |
+
200.000 - 299.999 kr.,4.0,1.0,64,,1.0,1441,3,0.5,
|
| 1281 |
+
600.000 - 699.999 kr.,5.0,0.0,54,,1.0,1442,3,0.5,
|
| 1282 |
+
Onsker ikke at oplyse,5.0,1.0,33,0.0,1.0,1444,3,0.5,0.0
|
| 1283 |
+
700.000 eller derover,5.0,1.0,85,0.0,1.0,1446,3,0.25,0.0
|
| 1284 |
+
500.000 - 599.999 kr.,4.0,0.0,43,1.0,1.0,1447,3,0.5,1.0
|
| 1285 |
+
700.000 eller derover,5.0,1.0,69,1.0,1.0,1448,3,0.75,1.0
|
| 1286 |
+
100.000 - 199.999 kr.,3.0,1.0,26,,1.0,1459,3,0.5,
|
| 1287 |
+
700.000 eller derover,5.0,1.0,56,0.0,1.0,1464,3,0.5,0.0
|
| 1288 |
+
200.000 - 299.999 kr.,3.0,1.0,56,0.0,1.0,1473,3,0.5,0.0
|
| 1289 |
+
500.000 - 599.999 kr.,4.0,1.0,46,,1.0,1477,3,1.0,
|
| 1290 |
+
100.000 - 199.999 kr.,3.0,0.0,25,1.0,1.0,1482,3,1.0,1.0
|
| 1291 |
+
600.000 - 699.999 kr.,5.0,1.0,55,0.0,1.0,1489,3,0.5,0.0
|
| 1292 |
+
600.000 - 699.999 kr.,5.0,1.0,59,1.0,1.0,1493,3,0.5,1.0
|
| 1293 |
+
400.000 - 499.999 kr.,1.0,1.0,56,1.0,1.0,1494,3,0.5,1.0
|
| 1294 |
+
700.000 eller derover,1.0,1.0,47,,1.0,1497,3,0.75,
|
| 1295 |
+
600.000 - 699.999 kr.,4.0,0.0,73,1.0,1.0,1499,3,1.0,1.0
|
| 1296 |
+
300.000 - 399.999 kr.,4.0,1.0,47,1.0,1.0,1501,3,1.0,1.0
|
| 1297 |
+
700.000 eller derover,2.0,0.0,55,,1.0,1504,3,0.5,
|
| 1298 |
+
600.000 - 699.999 kr.,4.0,1.0,57,1.0,1.0,1507,3,0.5,1.0
|
| 1299 |
+
700.000 eller derover,4.0,1.0,53,1.0,1.0,1508,3,0.75,1.0
|
| 1300 |
+
300.000 - 399.999 kr.,2.0,0.0,52,1.0,1.0,1511,3,0.75,1.0
|
| 1301 |
+
700.000 eller derover,4.0,1.0,44,0.0,1.0,1512,3,0.5,0.0
|
| 1302 |
+
600.000 - 699.999 kr.,4.0,1.0,46,1.0,1.0,1516,3,0.5,1.0
|
| 1303 |
+
700.000 eller derover,5.0,0.0,44,,1.0,1517,3,0.5,
|
| 1304 |
+
700.000 eller derover,5.0,0.0,40,0.0,1.0,1518,3,0.5,0.0
|
| 1305 |
+
700.000 eller derover,4.0,1.0,41,1.0,1.0,1521,3,0.25,1.0
|
| 1306 |
+
300.000 - 399.999 kr.,4.0,0.0,49,1.0,1.0,1523,3,0.5,1.0
|
| 1307 |
+
500.000 - 599.999 kr.,1.0,1.0,58,1.0,1.0,1528,3,0.5,1.0
|
| 1308 |
+
400.000 - 499.999 kr.,3.0,1.0,50,1.0,1.0,1530,3,0.75,1.0
|
| 1309 |
+
500.000 - 599.999 kr.,2.0,1.0,52,0.0,1.0,1532,3,0.5,0.0
|
| 1310 |
+
600.000 - 699.999 kr.,4.0,0.0,37,,1.0,1536,3,0.5,
|
| 1311 |
+
200.000 - 299.999 kr.,1.0,0.0,42,,1.0,1538,3,0.75,
|
| 1312 |
+
100.000 - 199.999 kr.,2.0,1.0,44,1.0,1.0,1542,3,1.0,1.0
|
| 1313 |
+
100.000 - 199.999 kr.,2.0,1.0,69,1.0,1.0,1544,3,0.5,1.0
|
| 1314 |
+
700.000 eller derover,2.0,1.0,65,1.0,1.0,1545,3,0.75,1.0
|
| 1315 |
+
600.000 - 699.999 kr.,4.0,1.0,35,,1.0,1548,3,0.25,
|
| 1316 |
+
Indtil 99.999 kr.,5.0,1.0,25,,1.0,1549,3,1.0,
|
| 1317 |
+
Onsker ikke at oplyse,2.0,1.0,45,,1.0,1551,3,0.75,
|
| 1318 |
+
200.000 - 299.999 kr.,4.0,0.0,59,1.0,1.0,1554,3,1.0,1.0
|
| 1319 |
+
400.000 - 499.999 kr.,2.0,1.0,42,0.0,1.0,1561,3,0.25,0.0
|
| 1320 |
+
700.000 eller derover,5.0,1.0,52,1.0,1.0,1562,3,0.25,1.0
|
| 1321 |
+
700.000 eller derover,3.0,0.0,19,,1.0,1565,3,0.75,
|
| 1322 |
+
700.000 eller derover,5.0,1.0,43,,1.0,1569,3,0.5,
|
| 1323 |
+
300.000 - 399.999 kr.,2.0,1.0,62,0.0,1.0,1578,3,0.75,0.0
|
| 1324 |
+
600.000 - 699.999 kr.,2.0,0.0,44,1.0,1.0,1587,3,0.75,1.0
|
| 1325 |
+
600.000 - 699.999 kr.,2.0,1.0,31,0.0,1.0,1592,3,0.75,0.0
|
| 1326 |
+
700.000 eller derover,4.0,1.0,37,0.0,1.0,1595,3,0.75,0.0
|
| 1327 |
+
700.000 eller derover,5.0,0.0,47,1.0,1.0,1598,3,0.75,1.0
|
| 1328 |
+
300.000 - 399.999 kr.,4.0,1.0,52,1.0,1.0,1599,3,0.5,1.0
|
| 1329 |
+
Onsker ikke at oplyse,4.0,0.0,65,,1.0,1611,3,0.5,
|
| 1330 |
+
700.000 eller derover,4.0,1.0,47,1.0,1.0,1612,3,0.75,1.0
|
| 1331 |
+
400.000 - 499.999 kr.,2.0,1.0,49,1.0,1.0,1614,3,0.5,1.0
|
| 1332 |
+
200.000 - 299.999 kr.,1.0,1.0,48,1.0,1.0,1616,3,0.5,1.0
|
| 1333 |
+
200.000 - 299.999 kr.,4.0,0.0,59,,1.0,1617,3,0.5,
|
| 1334 |
+
600.000 - 699.999 kr.,4.0,0.0,38,,1.0,1625,3,0.5,
|
| 1335 |
+
700.000 eller derover,5.0,1.0,34,,1.0,1634,3,0.5,
|
| 1336 |
+
400.000 - 499.999 kr.,4.0,0.0,55,,1.0,1636,3,0.5,
|
| 1337 |
+
100.000 - 199.999 kr.,4.0,0.0,65,,1.0,1637,3,0.5,
|
| 1338 |
+
300.000 - 399.999 kr.,4.0,1.0,51,0.0,1.0,1641,3,0.25,0.0
|
| 1339 |
+
600.000 - 699.999 kr.,2.0,0.0,46,,1.0,1648,3,0.25,
|
| 1340 |
+
300.000 - 399.999 kr.,4.0,0.0,64,1.0,1.0,1651,3,0.5,1.0
|
| 1341 |
+
500.000 - 599.999 kr.,5.0,1.0,28,0.0,1.0,1654,3,1.0,0.0
|
| 1342 |
+
300.000 - 399.999 kr.,4.0,1.0,31,,1.0,1664,3,0.5,
|
| 1343 |
+
200.000 - 299.999 kr.,4.0,0.0,56,1.0,1.0,1672,3,0.75,1.0
|
| 1344 |
+
200.000 - 299.999 kr.,3.0,1.0,29,,1.0,1673,3,0.75,
|
| 1345 |
+
700.000 eller derover,4.0,1.0,63,1.0,1.0,1674,3,0.75,1.0
|
| 1346 |
+
500.000 - 599.999 kr.,4.0,0.0,54,1.0,1.0,1675,3,0.5,1.0
|
| 1347 |
+
700.000 eller derover,5.0,0.0,49,1.0,1.0,1676,3,0.5,1.0
|
| 1348 |
+
700.000 eller derover,5.0,1.0,50,1.0,1.0,1677,3,1.0,1.0
|
| 1349 |
+
100.000 - 199.999 kr.,2.0,1.0,82,,1.0,1678,3,0.5,
|
| 1350 |
+
300.000 - 399.999 kr.,3.0,0.0,52,0.0,1.0,1681,3,0.5,0.0
|
| 1351 |
+
300.000 - 399.999 kr.,2.0,1.0,53,1.0,1.0,1692,3,0.75,1.0
|
| 1352 |
+
700.000 eller derover,5.0,1.0,55,0.0,1.0,1696,3,0.5,0.0
|
| 1353 |
+
700.000 eller derover,5.0,1.0,54,1.0,1.0,1698,3,0.75,1.0
|
| 1354 |
+
700.000 eller derover,4.0,0.0,21,0.0,1.0,1704,3,0.75,0.0
|
| 1355 |
+
400.000 - 499.999 kr.,5.0,1.0,45,,1.0,1705,3,0.0,
|
| 1356 |
+
700.000 eller derover,5.0,1.0,47,,1.0,1709,3,0.5,
|
| 1357 |
+
200.000 - 299.999 kr.,1.0,1.0,65,0.0,1.0,1714,3,0.25,0.0
|
| 1358 |
+
700.000 eller derover,4.0,1.0,36,0.0,1.0,1716,3,0.75,0.0
|
| 1359 |
+
600.000 - 699.999 kr.,2.0,1.0,49,0.0,1.0,1717,3,0.5,0.0
|
| 1360 |
+
500.000 - 599.999 kr.,3.0,1.0,37,0.0,1.0,1718,3,0.75,0.0
|
| 1361 |
+
500.000 - 599.999 kr.,2.0,0.0,49,0.0,1.0,1720,3,0.5,0.0
|
| 1362 |
+
500.000 - 599.999 kr.,3.0,0.0,40,0.0,1.0,1721,3,0.5,0.0
|
| 1363 |
+
300.000 - 399.999 kr.,4.0,1.0,52,,1.0,1722,3,0.5,
|
| 1364 |
+
600.000 - 699.999 kr.,4.0,1.0,57,0.0,1.0,1724,3,0.5,0.0
|
| 1365 |
+
400.000 - 499.999 kr.,4.0,1.0,57,0.0,1.0,1728,3,0.75,0.0
|
| 1366 |
+
100.000 - 199.999 kr.,5.0,1.0,25,,1.0,1729,3,0.75,
|
| 1367 |
+
300.000 - 399.999 kr.,5.0,0.0,58,,1.0,1735,3,1.0,
|
| 1368 |
+
400.000 - 499.999 kr.,5.0,1.0,47,1.0,1.0,1737,3,0.75,1.0
|
| 1369 |
+
700.000 eller derover,5.0,1.0,50,0.0,1.0,1741,3,0.5,0.0
|
| 1370 |
+
600.000 - 699.999 kr.,2.0,1.0,37,1.0,1.0,1753,3,0.75,1.0
|
| 1371 |
+
400.000 - 499.999 kr.,4.0,0.0,34,0.0,1.0,1754,3,0.25,0.0
|
| 1372 |
+
200.000 - 299.999 kr.,2.0,1.0,48,1.0,1.0,1758,3,0.5,1.0
|
| 1373 |
+
700.000 eller derover,4.0,1.0,64,,1.0,1760,3,0.75,
|
| 1374 |
+
300.000 - 399.999 kr.,4.0,1.0,65,,1.0,1764,3,0.5,
|
| 1375 |
+
500.000 - 599.999 kr.,4.0,1.0,58,1.0,1.0,1765,3,0.5,1.0
|
| 1376 |
+
500.000 - 599.999 kr.,2.0,1.0,58,,1.0,1768,3,0.25,
|
| 1377 |
+
300.000 - 399.999 kr.,4.0,0.0,75,0.0,1.0,1770,3,0.75,0.0
|
| 1378 |
+
400.000 - 499.999 kr.,2.0,1.0,40,,1.0,1777,3,0.25,
|
| 1379 |
+
600.000 - 699.999 kr.,5.0,1.0,59,,1.0,1779,3,0.5,
|
| 1380 |
+
200.000 - 299.999 kr.,2.0,0.0,65,0.0,1.0,1782,3,0.5,0.0
|
| 1381 |
+
700.000 eller derover,4.0,1.0,51,,1.0,1785,3,1.0,
|
| 1382 |
+
500.000 - 599.999 kr.,4.0,1.0,50,1.0,1.0,1786,3,0.75,1.0
|
| 1383 |
+
700.000 eller derover,4.0,0.0,56,,1.0,1787,3,1.0,
|
| 1384 |
+
700.000 eller derover,5.0,1.0,33,0.0,1.0,1789,3,0.25,0.0
|
| 1385 |
+
500.000 - 599.999 kr.,2.0,1.0,60,0.0,1.0,1793,3,0.25,0.0
|
| 1386 |
+
700.000 eller derover,4.0,1.0,35,0.0,1.0,1796,3,0.75,0.0
|
| 1387 |
+
300.000 - 399.999 kr.,2.0,1.0,63,1.0,1.0,1800,3,0.5,1.0
|
| 1388 |
+
300.000 - 399.999 kr.,2.0,1.0,39,,1.0,1806,3,0.25,
|
| 1389 |
+
600.000 - 699.999 kr.,5.0,1.0,35,,1.0,1814,3,0.5,
|
| 1390 |
+
300.000 - 399.999 kr.,5.0,1.0,31,1.0,1.0,1816,3,0.5,1.0
|
| 1391 |
+
700.000 eller derover,2.0,1.0,30,1.0,1.0,1817,3,0.75,1.0
|
| 1392 |
+
500.000 - 599.999 kr.,5.0,1.0,63,0.0,1.0,1822,3,0.5,0.0
|
| 1393 |
+
700.000 eller derover,4.0,1.0,48,,1.0,1824,3,0.5,
|
| 1394 |
+
Onsker ikke at oplyse,4.0,1.0,60,1.0,1.0,1831,3,0.75,1.0
|
| 1395 |
+
400.000 - 499.999 kr.,4.0,1.0,39,,1.0,1836,3,0.75,
|
| 1396 |
+
700.000 eller derover,3.0,1.0,23,,1.0,1844,3,0.25,
|
| 1397 |
+
400.000 - 499.999 kr.,4.0,1.0,64,,1.0,1846,3,1.0,
|
| 1398 |
+
500.000 - 599.999 kr.,4.0,1.0,46,,1.0,1854,3,0.75,
|
| 1399 |
+
600.000 - 699.999 kr.,4.0,1.0,38,0.0,1.0,1855,3,0.5,0.0
|
| 1400 |
+
400.000 - 499.999 kr.,4.0,0.0,42,,1.0,1869,3,0.75,
|
| 1401 |
+
500.000 - 599.999 kr.,2.0,1.0,67,,1.0,1871,3,0.5,
|
| 1402 |
+
600.000 - 699.999 kr.,3.0,1.0,52,1.0,1.0,1877,3,0.5,1.0
|
| 1403 |
+
Onsker ikke at oplyse,4.0,1.0,41,0.0,1.0,1882,3,0.5,0.0
|
| 1404 |
+
200.000 - 299.999 kr.,5.0,1.0,54,0.0,1.0,1889,3,0.5,0.0
|
| 1405 |
+
400.000 - 499.999 kr.,2.0,1.0,10,1.0,1.0,1891,3,0.5,1.0
|
| 1406 |
+
Onsker ikke at oplyse,4.0,0.0,29,,1.0,1897,3,0.75,
|
| 1407 |
+
500.000 - 599.999 kr.,4.0,0.0,50,1.0,1.0,1898,3,0.75,1.0
|
| 1408 |
+
700.000 eller derover,4.0,0.0,61,,1.0,1901,3,0.75,
|
| 1409 |
+
Onsker ikke at oplyse,5.0,1.0,41,0.0,1.0,1902,3,0.75,0.0
|
| 1410 |
+
300.000 - 399.999 kr.,4.0,0.0,48,1.0,1.0,1903,3,0.75,1.0
|
| 1411 |
+
700.000 eller derover,2.0,1.0,53,1.0,1.0,1907,3,1.0,1.0
|
| 1412 |
+
400.000 - 499.999 kr.,4.0,0.0,62,1.0,1.0,1924,3,0.75,1.0
|
| 1413 |
+
100.000 - 199.999 kr.,4.0,1.0,25,,1.0,1925,3,0.5,
|
| 1414 |
+
600.000 - 699.999 kr.,4.0,1.0,52,0.0,1.0,1927,3,0.0,0.0
|
| 1415 |
+
Onsker ikke at oplyse,4.0,1.0,33,1.0,1.0,1928,3,0.75,1.0
|
| 1416 |
+
700.000 eller derover,5.0,1.0,36,1.0,1.0,1933,3,0.25,1.0
|
| 1417 |
+
700.000 eller derover,4.0,1.0,54,,1.0,1935,3,0.5,
|
| 1418 |
+
300.000 - 399.999 kr.,4.0,0.0,65,0.0,1.0,1937,3,0.5,0.0
|
| 1419 |
+
700.000 eller derover,5.0,1.0,46,0.0,1.0,1938,3,0.25,0.0
|
| 1420 |
+
700.000 eller derover,4.0,1.0,49,1.0,1.0,1939,3,0.75,1.0
|
| 1421 |
+
400.000 - 499.999 kr.,4.0,0.0,61,0.0,1.0,1941,3,1.0,0.0
|
| 1422 |
+
600.000 - 699.999 kr.,2.0,1.0,55,1.0,1.0,1944,3,0.5,1.0
|
| 1423 |
+
700.000 eller derover,4.0,0.0,57,1.0,1.0,1950,3,0.5,1.0
|
| 1424 |
+
400.000 - 499.999 kr.,4.0,1.0,51,0.0,1.0,1951,3,0.75,0.0
|
| 1425 |
+
500.000 - 599.999 kr.,4.0,1.0,42,0.0,1.0,1957,3,0.5,0.0
|
| 1426 |
+
200.000 - 299.999 kr.,4.0,0.0,61,,1.0,1960,3,0.75,
|
| 1427 |
+
200.000 - 299.999 kr.,2.0,1.0,64,0.0,1.0,1961,3,0.75,0.0
|
| 1428 |
+
700.000 eller derover,4.0,1.0,50,0.0,1.0,1967,3,0.75,0.0
|
| 1429 |
+
400.000 - 499.999 kr.,5.0,1.0,63,1.0,1.0,1974,3,0.75,1.0
|
| 1430 |
+
600.000 - 699.999 kr.,3.0,1.0,53,,1.0,1976,3,0.75,
|
| 1431 |
+
700.000 eller derover,5.0,1.0,48,0.0,1.0,1985,3,0.5,0.0
|
| 1432 |
+
500.000 - 599.999 kr.,4.0,0.0,50,1.0,1.0,1987,3,0.5,1.0
|
| 1433 |
+
700.000 eller derover,5.0,0.0,61,1.0,1.0,1988,3,0.75,1.0
|
| 1434 |
+
200.000 - 299.999 kr.,4.0,0.0,76,0.0,1.0,1993,3,1.0,0.0
|
| 1435 |
+
400.000 - 499.999 kr.,4.0,1.0,58,0.0,1.0,1995,3,0.75,0.0
|
| 1436 |
+
Onsker ikke at oplyse,4.0,1.0,61,0.0,1.0,1996,3,1.0,0.0
|
| 1437 |
+
500.000 - 599.999 kr.,4.0,1.0,56,0.0,1.0,1999,3,0.5,0.0
|
| 1438 |
+
700.000 eller derover,4.0,1.0,40,0.0,1.0,2000,3,0.5,0.0
|
| 1439 |
+
100.000 - 199.999 kr.,1.0,1.0,48,1.0,1.0,2006,3,0.25,1.0
|
| 1440 |
+
Onsker ikke at oplyse,3.0,0.0,40,0.0,1.0,2009,3,0.75,0.0
|
| 1441 |
+
600.000 - 699.999 kr.,3.0,1.0,51,1.0,1.0,2011,3,0.5,1.0
|
| 1442 |
+
700.000 eller derover,4.0,1.0,45,0.0,1.0,2017,3,0.75,0.0
|
| 1443 |
+
Onsker ikke at oplyse,5.0,0.0,39,1.0,1.0,2022,3,0.5,1.0
|
| 1444 |
+
700.000 eller derover,4.0,0.0,45,,1.0,2025,3,0.75,
|
| 1445 |
+
100.000 - 199.999 kr.,2.0,1.0,57,1.0,1.0,2033,3,0.75,1.0
|
| 1446 |
+
700.000 eller derover,5.0,1.0,57,1.0,1.0,2035,3,0.5,1.0
|
| 1447 |
+
400.000 - 499.999 kr.,4.0,0.0,59,1.0,1.0,2039,3,0.75,1.0
|
| 1448 |
+
400.000 - 499.999 kr.,4.0,1.0,45,,1.0,2040,3,0.5,
|
| 1449 |
+
Onsker ikke at oplyse,4.0,0.0,59,1.0,1.0,2048,3,0.75,1.0
|
| 1450 |
+
500.000 - 599.999 kr.,2.0,1.0,52,,1.0,2052,3,0.75,
|
| 1451 |
+
400.000 - 499.999 kr.,2.0,0.0,41,0.0,1.0,2056,3,0.5,0.0
|
| 1452 |
+
600.000 - 699.999 kr.,4.0,0.0,46,1.0,1.0,2060,3,0.5,1.0
|
| 1453 |
+
700.000 eller derover,5.0,0.0,42,1.0,1.0,2066,3,0.5,1.0
|
| 1454 |
+
400.000 - 499.999 kr.,5.0,0.0,34,1.0,1.0,2068,3,0.75,1.0
|
| 1455 |
+
300.000 - 399.999 kr.,5.0,0.0,49,,1.0,2073,3,0.5,
|
| 1456 |
+
300.000 - 399.999 kr.,5.0,1.0,53,,1.0,2074,3,0.5,
|
| 1457 |
+
100.000 - 199.999 kr.,4.0,1.0,25,0.0,1.0,2075,3,0.75,0.0
|
| 1458 |
+
400.000 - 499.999 kr.,5.0,0.0,41,1.0,1.0,2076,3,0.5,1.0
|
| 1459 |
+
400.000 - 499.999 kr.,2.0,0.0,54,1.0,1.0,2077,3,0.75,1.0
|
| 1460 |
+
300.000 - 399.999 kr.,4.0,1.0,32,0.0,1.0,2083,3,0.75,0.0
|
| 1461 |
+
700.000 eller derover,4.0,1.0,34,0.0,1.0,2085,3,0.5,0.0
|
| 1462 |
+
100.000 - 199.999 kr.,3.0,1.0,22,1.0,1.0,2090,3,0.25,1.0
|
| 1463 |
+
200.000 - 299.999 kr.,2.0,0.0,51,1.0,1.0,2094,3,0.75,1.0
|
| 1464 |
+
Onsker ikke at oplyse,5.0,1.0,57,,1.0,2114,3,0.75,
|
| 1465 |
+
300.000 - 399.999 kr.,4.0,1.0,66,,1.0,2118,3,0.75,
|
| 1466 |
+
500.000 - 599.999 kr.,5.0,1.0,34,0.0,1.0,2128,3,0.75,0.0
|
| 1467 |
+
400.000 - 499.999 kr.,4.0,1.0,60,0.0,1.0,2130,3,0.0,0.0
|
| 1468 |
+
400.000 - 499.999 kr.,4.0,0.0,57,0.0,1.0,2135,3,0.5,0.0
|
| 1469 |
+
100.000 - 199.999 kr.,3.0,1.0,42,,1.0,2139,3,0.5,
|
| 1470 |
+
700.000 eller derover,5.0,1.0,37,0.0,1.0,2144,3,0.5,0.0
|
| 1471 |
+
Onsker ikke at oplyse,4.0,0.0,50,1.0,1.0,2145,3,0.5,1.0
|
| 1472 |
+
500.000 - 599.999 kr.,5.0,1.0,71,1.0,1.0,2146,3,1.0,1.0
|
| 1473 |
+
300.000 - 399.999 kr.,1.0,0.0,63,0.0,1.0,2147,3,0.75,0.0
|
| 1474 |
+
500.000 - 599.999 kr.,2.0,0.0,56,1.0,1.0,2148,3,1.0,1.0
|
| 1475 |
+
200.000 - 299.999 kr.,4.0,1.0,65,0.0,1.0,2150,3,0.5,0.0
|
| 1476 |
+
300.000 - 399.999 kr.,2.0,1.0,29,,1.0,2155,3,0.75,
|
| 1477 |
+
300.000 - 399.999 kr.,5.0,1.0,45,0.0,1.0,2156,3,0.5,0.0
|
| 1478 |
+
Onsker ikke at oplyse,5.0,0.0,53,0.0,1.0,2159,3,0.5,0.0
|
| 1479 |
+
400.000 - 499.999 kr.,3.0,0.0,36,,1.0,2166,3,0.5,
|
| 1480 |
+
400.000 - 499.999 kr.,4.0,0.0,52,,1.0,2171,3,0.5,
|
| 1481 |
+
400.000 - 499.999 kr.,5.0,1.0,32,,1.0,2172,3,0.75,
|
| 1482 |
+
600.000 - 699.999 kr.,3.0,1.0,52,,1.0,2177,3,0.75,
|
| 1483 |
+
200.000 - 299.999 kr.,5.0,0.0,58,0.0,1.0,2178,3,0.5,0.0
|
| 1484 |
+
600.000 - 699.999 kr.,4.0,1.0,52,1.0,1.0,2181,3,0.25,1.0
|
| 1485 |
+
200.000 - 299.999 kr.,4.0,0.0,56,1.0,1.0,2184,3,0.75,1.0
|
| 1486 |
+
400.000 - 499.999 kr.,4.0,1.0,61,1.0,1.0,2186,3,0.25,1.0
|
| 1487 |
+
700.000 eller derover,4.0,1.0,56,1.0,1.0,2198,3,1.0,1.0
|
| 1488 |
+
100.000 - 199.999 kr.,1.0,0.0,64,,1.0,2215,3,0.75,
|
| 1489 |
+
Onsker ikke at oplyse,4.0,0.0,47,1.0,1.0,2219,3,0.5,1.0
|
| 1490 |
+
500.000 - 599.999 kr.,4.0,0.0,49,1.0,1.0,2220,3,0.5,1.0
|
| 1491 |
+
Indtil 99.999 kr.,3.0,0.0,22,1.0,1.0,2224,3,0.75,1.0
|
| 1492 |
+
Onsker ikke at oplyse,2.0,1.0,62,0.0,1.0,2226,3,0.25,0.0
|
| 1493 |
+
Onsker ikke at oplyse,2.0,1.0,53,1.0,1.0,2228,3,0.25,1.0
|
| 1494 |
+
300.000 - 399.999 kr.,3.0,1.0,58,1.0,1.0,2230,3,0.5,1.0
|
| 1495 |
+
400.000 - 499.999 kr.,2.0,1.0,33,,1.0,2231,3,0.5,
|
| 1496 |
+
100.000 - 199.999 kr.,3.0,0.0,21,1.0,1.0,2234,3,0.25,1.0
|
| 1497 |
+
600.000 - 699.999 kr.,4.0,1.0,57,1.0,1.0,2242,3,0.5,1.0
|
| 1498 |
+
400.000 - 499.999 kr.,5.0,1.0,66,1.0,1.0,2247,3,0.75,1.0
|
| 1499 |
+
200.000 - 299.999 kr.,4.0,0.0,39,1.0,1.0,2248,3,1.0,1.0
|
| 1500 |
+
700.000 eller derover,2.0,0.0,47,1.0,1.0,2249,3,0.5,1.0
|
| 1501 |
+
700.000 eller derover,5.0,0.0,39,,1.0,2251,3,0.5,
|
| 1502 |
+
100.000 - 199.999 kr.,1.0,1.0,60,,1.0,2254,3,1.0,
|
| 1503 |
+
700.000 eller derover,5.0,0.0,40,0.0,1.0,2255,3,0.5,0.0
|
| 1504 |
+
700.000 eller derover,4.0,1.0,55,,1.0,2256,3,0.75,
|
| 1505 |
+
400.000 - 499.999 kr.,5.0,0.0,58,1.0,1.0,2257,3,0.5,1.0
|
| 1506 |
+
300.000 - 399.999 kr.,5.0,1.0,28,,1.0,2262,3,0.5,
|
| 1507 |
+
200.000 - 299.999 kr.,4.0,1.0,66,1.0,1.0,2266,3,1.0,1.0
|
| 1508 |
+
700.000 eller derover,4.0,1.0,57,0.0,1.0,2267,3,0.75,0.0
|
| 1509 |
+
500.000 - 599.999 kr.,4.0,0.0,29,1.0,1.0,2274,3,0.5,1.0
|
| 1510 |
+
200.000 - 299.999 kr.,4.0,0.0,49,1.0,1.0,2275,3,0.75,1.0
|
| 1511 |
+
500.000 - 599.999 kr.,3.0,1.0,53,,1.0,2282,3,0.5,
|
| 1512 |
+
400.000 - 499.999 kr.,3.0,0.0,20,1.0,1.0,2283,3,0.5,1.0
|
| 1513 |
+
Onsker ikke at oplyse,3.0,1.0,18,0.0,1.0,2286,3,0.5,0.0
|
| 1514 |
+
300.000 - 399.999 kr.,4.0,0.0,66,1.0,1.0,2290,3,0.75,1.0
|
| 1515 |
+
100.000 - 199.999 kr.,1.0,1.0,80,1.0,1.0,2291,3,0.5,1.0
|
| 1516 |
+
300.000 - 399.999 kr.,1.0,0.0,40,,1.0,2293,3,0.5,
|
| 1517 |
+
500.000 - 599.999 kr.,4.0,1.0,58,0.0,1.0,2297,3,1.0,0.0
|
| 1518 |
+
300.000 - 399.999 kr.,4.0,0.0,63,0.0,1.0,2298,3,0.75,0.0
|
| 1519 |
+
700.000 eller derover,3.0,0.0,44,1.0,1.0,2303,3,0.5,1.0
|
| 1520 |
+
Onsker ikke at oplyse,1.0,1.0,53,1.0,1.0,2304,3,1.0,1.0
|
| 1521 |
+
100.000 - 199.999 kr.,4.0,1.0,68,,1.0,2306,3,0.5,
|
| 1522 |
+
600.000 - 699.999 kr.,2.0,1.0,51,0.0,1.0,2316,3,0.25,0.0
|
| 1523 |
+
300.000 - 399.999 kr.,4.0,1.0,74,0.0,1.0,2317,3,1.0,0.0
|
| 1524 |
+
Indtil 99.999 kr.,3.0,1.0,24,,1.0,2322,3,0.75,
|
| 1525 |
+
300.000 - 399.999 kr.,4.0,0.0,58,1.0,1.0,2324,3,0.25,1.0
|
| 1526 |
+
400.000 - 499.999 kr.,1.0,1.0,62,,1.0,2330,3,0.5,
|
| 1527 |
+
300.000 - 399.999 kr.,5.0,1.0,54,1.0,1.0,2333,3,0.5,1.0
|
| 1528 |
+
400.000 - 499.999 kr.,4.0,1.0,30,,1.0,2335,3,0.5,
|
| 1529 |
+
700.000 eller derover,5.0,1.0,45,0.0,1.0,2340,3,0.5,0.0
|
| 1530 |
+
700.000 eller derover,4.0,0.0,46,0.0,1.0,2350,3,0.75,0.0
|
| 1531 |
+
600.000 - 699.999 kr.,5.0,1.0,28,0.0,1.0,2352,3,0.75,0.0
|
| 1532 |
+
700.000 eller derover,4.0,0.0,44,1.0,1.0,2353,3,0.5,1.0
|
| 1533 |
+
400.000 - 499.999 kr.,2.0,1.0,49,,1.0,2354,3,0.75,
|
| 1534 |
+
300.000 - 399.999 kr.,4.0,0.0,59,0.0,1.0,2356,3,0.5,0.0
|
| 1535 |
+
200.000 - 299.999 kr.,2.0,0.0,51,1.0,1.0,2359,3,1.0,1.0
|
| 1536 |
+
500.000 - 599.999 kr.,2.0,1.0,42,,1.0,2363,3,0.5,
|
| 1537 |
+
400.000 - 499.999 kr.,4.0,1.0,42,1.0,1.0,2364,3,0.25,1.0
|
| 1538 |
+
300.000 - 399.999 kr.,2.0,0.0,47,1.0,1.0,2366,3,0.75,1.0
|
| 1539 |
+
300.000 - 399.999 kr.,3.0,1.0,35,0.0,1.0,2367,3,0.75,0.0
|
| 1540 |
+
700.000 eller derover,4.0,1.0,54,0.0,1.0,2371,3,0.75,0.0
|
| 1541 |
+
300.000 - 399.999 kr.,4.0,1.0,42,,1.0,2372,3,0.5,
|
| 1542 |
+
400.000 - 499.999 kr.,3.0,1.0,51,0.0,1.0,2374,3,0.75,0.0
|
| 1543 |
+
700.000 eller derover,5.0,1.0,56,0.0,1.0,2406,3,0.75,0.0
|
| 1544 |
+
400.000 - 499.999 kr.,5.0,0.0,44,,1.0,2411,3,0.5,
|
| 1545 |
+
700.000 eller derover,5.0,1.0,62,,1.0,2415,3,0.5,
|
| 1546 |
+
300.000 - 399.999 kr.,2.0,1.0,30,,1.0,2416,3,0.75,
|
| 1547 |
+
700.000 eller derover,5.0,1.0,51,1.0,1.0,2417,3,0.25,1.0
|
| 1548 |
+
300.000 - 399.999 kr.,4.0,1.0,52,,1.0,2425,3,0.25,
|
| 1549 |
+
500.000 - 599.999 kr.,2.0,1.0,55,1.0,1.0,2427,3,1.0,1.0
|
| 1550 |
+
700.000 eller derover,5.0,1.0,50,0.0,1.0,2428,3,0.75,0.0
|
| 1551 |
+
500.000 - 599.999 kr.,4.0,1.0,42,0.0,1.0,2435,3,0.5,0.0
|
| 1552 |
+
500.000 - 599.999 kr.,4.0,1.0,62,1.0,1.0,2442,3,0.25,1.0
|
| 1553 |
+
700.000 eller derover,4.0,1.0,50,0.0,1.0,2445,3,0.5,0.0
|
| 1554 |
+
600.000 - 699.999 kr.,5.0,0.0,45,1.0,1.0,2450,3,1.0,1.0
|
| 1555 |
+
500.000 - 599.999 kr.,1.0,1.0,68,1.0,1.0,2451,3,1.0,1.0
|
| 1556 |
+
200.000 - 299.999 kr.,3.0,0.0,32,,1.0,2457,3,0.75,
|
| 1557 |
+
500.000 - 599.999 kr.,4.0,0.0,29,,1.0,2458,3,0.5,
|
| 1558 |
+
700.000 eller derover,2.0,1.0,46,,1.0,2459,3,0.5,
|
| 1559 |
+
300.000 - 399.999 kr.,2.0,1.0,55,1.0,1.0,2462,3,0.25,1.0
|
| 1560 |
+
600.000 - 699.999 kr.,4.0,1.0,34,1.0,1.0,2466,3,1.0,1.0
|
| 1561 |
+
700.000 eller derover,3.0,1.0,49,0.0,1.0,2467,3,0.5,0.0
|
| 1562 |
+
600.000 - 699.999 kr.,4.0,1.0,42,1.0,1.0,2470,3,0.75,1.0
|
| 1563 |
+
100.000 - 199.999 kr.,1.0,1.0,65,,1.0,2479,3,0.75,
|
| 1564 |
+
700.000 eller derover,2.0,1.0,48,1.0,1.0,2480,3,0.5,1.0
|
| 1565 |
+
700.000 eller derover,4.0,0.0,43,0.0,1.0,2482,3,0.25,0.0
|
| 1566 |
+
Onsker ikke at oplyse,4.0,0.0,63,0.0,1.0,2483,3,0.75,0.0
|
| 1567 |
+
600.000 - 699.999 kr.,5.0,0.0,37,0.0,1.0,2485,3,0.5,0.0
|
| 1568 |
+
200.000 - 299.999 kr.,1.0,1.0,52,1.0,1.0,2486,3,0.25,1.0
|
| 1569 |
+
400.000 - 499.999 kr.,5.0,0.0,50,,1.0,2489,3,0.5,
|
| 1570 |
+
Onsker ikke at oplyse,5.0,1.0,31,,1.0,2491,3,0.75,
|
| 1571 |
+
600.000 - 699.999 kr.,2.0,0.0,37,0.0,1.0,2493,3,0.25,0.0
|
| 1572 |
+
300.000 - 399.999 kr.,2.0,1.0,64,,1.0,2494,3,0.25,
|
| 1573 |
+
400.000 - 499.999 kr.,4.0,1.0,27,0.0,1.0,2498,3,1.0,0.0
|
| 1574 |
+
700.000 eller derover,5.0,1.0,41,,1.0,2506,3,0.75,
|
| 1575 |
+
400.000 - 499.999 kr.,4.0,1.0,41,,1.0,2507,3,0.5,
|
| 1576 |
+
700.000 eller derover,5.0,0.0,33,1.0,1.0,2508,3,0.25,1.0
|
| 1577 |
+
700.000 eller derover,4.0,1.0,47,,1.0,2509,3,0.75,
|
| 1578 |
+
400.000 - 499.999 kr.,4.0,0.0,51,,1.0,2511,3,1.0,
|
| 1579 |
+
300.000 - 399.999 kr.,4.0,1.0,65,1.0,1.0,2512,3,0.75,1.0
|
| 1580 |
+
Onsker ikke at oplyse,4.0,1.0,62,,1.0,2513,3,0.5,
|
| 1581 |
+
300.000 - 399.999 kr.,4.0,1.0,67,0.0,1.0,2516,3,0.25,0.0
|
| 1582 |
+
300.000 - 399.999 kr.,4.0,0.0,42,0.0,1.0,2517,3,0.5,0.0
|
| 1583 |
+
700.000 eller derover,5.0,1.0,32,1.0,1.0,2519,3,0.75,1.0
|
| 1584 |
+
100.000 - 199.999 kr.,5.0,0.0,29,,1.0,2521,3,0.5,
|
| 1585 |
+
200.000 - 299.999 kr.,2.0,1.0,24,0.0,1.0,2525,3,0.5,0.0
|
| 1586 |
+
300.000 - 399.999 kr.,4.0,1.0,52,1.0,1.0,2530,3,1.0,1.0
|
| 1587 |
+
400.000 - 499.999 kr.,4.0,1.0,57,1.0,1.0,2531,3,0.75,1.0
|
| 1588 |
+
700.000 eller derover,5.0,1.0,58,,1.0,2534,3,0.5,
|
| 1589 |
+
Onsker ikke at oplyse,1.0,1.0,68,0.0,1.0,2537,3,1.0,0.0
|
| 1590 |
+
700.000 eller derover,4.0,1.0,59,,1.0,2546,3,0.5,
|
| 1591 |
+
600.000 - 699.999 kr.,2.0,1.0,51,0.0,1.0,2548,3,0.75,0.0
|
| 1592 |
+
Onsker ikke at oplyse,2.0,1.0,49,1.0,1.0,2553,3,0.75,1.0
|
| 1593 |
+
600.000 - 699.999 kr.,5.0,1.0,35,0.0,1.0,2556,3,1.0,0.0
|
| 1594 |
+
600.000 - 699.999 kr.,5.0,1.0,63,0.0,1.0,2561,3,0.5,0.0
|
| 1595 |
+
400.000 - 499.999 kr.,5.0,1.0,54,,1.0,2566,3,1.0,
|
| 1596 |
+
700.000 eller derover,4.0,0.0,36,0.0,1.0,2572,3,0.75,0.0
|
| 1597 |
+
Onsker ikke at oplyse,5.0,1.0,59,,1.0,2582,3,0.25,
|
| 1598 |
+
600.000 - 699.999 kr.,4.0,0.0,51,,1.0,2584,3,0.75,
|
| 1599 |
+
400.000 - 499.999 kr.,1.0,1.0,64,,1.0,2588,3,0.5,
|
| 1600 |
+
300.000 - 399.999 kr.,2.0,1.0,45,1.0,1.0,2589,3,0.75,1.0
|
| 1601 |
+
700.000 eller derover,4.0,0.0,52,,1.0,2599,3,0.5,
|
| 1602 |
+
300.000 - 399.999 kr.,4.0,0.0,44,0.0,1.0,2601,3,0.5,0.0
|
| 1603 |
+
200.000 - 299.999 kr.,4.0,1.0,55,1.0,1.0,2608,3,0.25,1.0
|
| 1604 |
+
300.000 - 399.999 kr.,1.0,0.0,58,0.0,1.0,2610,3,0.5,0.0
|
| 1605 |
+
200.000 - 299.999 kr.,5.0,0.0,31,1.0,1.0,2612,3,0.25,1.0
|
| 1606 |
+
400.000 - 499.999 kr.,5.0,1.0,57,0.0,1.0,2615,3,0.5,0.0
|
| 1607 |
+
600.000 - 699.999 kr.,4.0,1.0,63,,1.0,2617,3,0.5,
|
| 1608 |
+
400.000 - 499.999 kr.,5.0,1.0,42,1.0,1.0,2620,3,0.25,1.0
|
| 1609 |
+
600.000 - 699.999 kr.,2.0,0.0,44,0.0,1.0,2623,3,0.5,0.0
|
| 1610 |
+
400.000 - 499.999 kr.,2.0,1.0,43,0.0,1.0,2628,3,0.75,0.0
|
| 1611 |
+
200.000 - 299.999 kr.,3.0,1.0,28,,1.0,2629,3,0.5,
|
| 1612 |
+
700.000 eller derover,1.0,1.0,43,1.0,1.0,2634,3,0.5,1.0
|
| 1613 |
+
100.000 - 199.999 kr.,2.0,0.0,39,0.0,1.0,2637,3,0.75,0.0
|
| 1614 |
+
400.000 - 499.999 kr.,5.0,0.0,2,1.0,1.0,2648,3,0.5,1.0
|
| 1615 |
+
600.000 - 699.999 kr.,4.0,1.0,54,,1.0,2653,3,1.0,
|
| 1616 |
+
600.000 - 699.999 kr.,5.0,1.0,55,1.0,1.0,2660,3,0.5,1.0
|
| 1617 |
+
500.000 - 599.999 kr.,4.0,1.0,50,,1.0,2663,3,0.25,
|
| 1618 |
+
100.000 - 199.999 kr.,2.0,1.0,67,1.0,1.0,2670,3,0.5,1.0
|
| 1619 |
+
700.000 eller derover,4.0,1.0,41,,1.0,2672,3,0.5,
|
| 1620 |
+
700.000 eller derover,5.0,0.0,55,1.0,1.0,2675,3,0.5,1.0
|
| 1621 |
+
700.000 eller derover,2.0,1.0,62,1.0,1.0,2677,3,0.75,1.0
|
| 1622 |
+
500.000 - 599.999 kr.,4.0,1.0,48,1.0,1.0,2678,3,0.5,1.0
|
| 1623 |
+
600.000 - 699.999 kr.,4.0,1.0,56,1.0,1.0,2686,3,0.5,1.0
|
| 1624 |
+
200.000 - 299.999 kr.,2.0,1.0,50,1.0,1.0,2692,3,0.75,1.0
|
| 1625 |
+
300.000 - 399.999 kr.,4.0,0.0,55,1.0,1.0,2693,3,0.5,1.0
|
| 1626 |
+
700.000 eller derover,2.0,1.0,53,0.0,1.0,2697,3,0.5,0.0
|
| 1627 |
+
700.000 eller derover,1.0,1.0,43,1.0,1.0,2699,3,0.75,1.0
|
| 1628 |
+
500.000 - 599.999 kr.,4.0,1.0,68,1.0,1.0,2700,3,0.75,1.0
|
| 1629 |
+
500.000 - 599.999 kr.,2.0,1.0,63,1.0,1.0,2701,3,0.75,1.0
|
| 1630 |
+
200.000 - 299.999 kr.,2.0,1.0,35,,1.0,2709,3,0.5,
|
| 1631 |
+
200.000 - 299.999 kr.,3.0,0.0,60,1.0,1.0,2711,3,0.5,1.0
|
| 1632 |
+
100.000 - 199.999 kr.,3.0,1.0,24,0.0,1.0,2712,3,0.5,0.0
|
| 1633 |
+
400.000 - 499.999 kr.,4.0,1.0,73,1.0,1.0,2717,3,0.75,1.0
|
| 1634 |
+
200.000 - 299.999 kr.,1.0,0.0,53,1.0,1.0,2720,3,0.5,1.0
|
| 1635 |
+
400.000 - 499.999 kr.,4.0,0.0,45,1.0,1.0,2722,3,0.5,1.0
|
| 1636 |
+
600.000 - 699.999 kr.,5.0,1.0,33,0.0,1.0,2723,3,0.75,0.0
|
| 1637 |
+
400.000 - 499.999 kr.,3.0,0.0,43,,1.0,2725,3,0.75,
|
| 1638 |
+
600.000 - 699.999 kr.,4.0,1.0,56,,1.0,2737,3,0.75,
|
| 1639 |
+
400.000 - 499.999 kr.,4.0,1.0,45,,1.0,2741,3,0.5,
|
| 1640 |
+
300.000 - 399.999 kr.,2.0,1.0,59,1.0,1.0,2742,3,0.25,1.0
|
| 1641 |
+
700.000 eller derover,4.0,0.0,54,0.0,1.0,2751,3,1.0,0.0
|
| 1642 |
+
200.000 - 299.999 kr.,4.0,1.0,68,,1.0,2753,3,0.5,
|
| 1643 |
+
400.000 - 499.999 kr.,5.0,1.0,65,0.0,1.0,2755,3,1.0,0.0
|
| 1644 |
+
400.000 - 499.999 kr.,4.0,1.0,64,0.0,1.0,2765,3,0.75,0.0
|
| 1645 |
+
500.000 - 599.999 kr.,4.0,1.0,51,0.0,1.0,2767,3,0.25,0.0
|
| 1646 |
+
400.000 - 499.999 kr.,4.0,0.0,58,,1.0,2769,3,0.5,
|
| 1647 |
+
600.000 - 699.999 kr.,4.0,1.0,55,0.0,1.0,2770,3,1.0,0.0
|
| 1648 |
+
300.000 - 399.999 kr.,5.0,1.0,55,1.0,1.0,2771,3,0.5,1.0
|
| 1649 |
+
700.000 eller derover,4.0,0.0,50,0.0,1.0,2772,3,0.25,0.0
|
| 1650 |
+
300.000 - 399.999 kr.,5.0,1.0,28,1.0,1.0,2774,3,0.75,1.0
|
| 1651 |
+
700.000 eller derover,4.0,0.0,52,1.0,1.0,2776,3,0.75,1.0
|
| 1652 |
+
300.000 - 399.999 kr.,4.0,0.0,44,1.0,1.0,2779,3,0.5,1.0
|
| 1653 |
+
Onsker ikke at oplyse,4.0,1.0,60,,1.0,2780,3,1.0,
|
| 1654 |
+
400.000 - 499.999 kr.,4.0,1.0,65,1.0,1.0,2782,3,0.25,1.0
|
| 1655 |
+
700.000 eller derover,4.0,0.0,41,,1.0,2785,3,0.5,
|
| 1656 |
+
300.000 - 399.999 kr.,4.0,1.0,65,0.0,1.0,2786,3,1.0,0.0
|
| 1657 |
+
500.000 - 599.999 kr.,2.0,1.0,65,,1.0,2792,3,0.5,
|
| 1658 |
+
400.000 - 499.999 kr.,1.0,1.0,39,,1.0,2793,3,0.75,
|
| 1659 |
+
Onsker ikke at oplyse,5.0,1.0,47,1.0,1.0,2794,3,0.5,1.0
|
| 1660 |
+
500.000 - 599.999 kr.,4.0,0.0,62,,1.0,2799,3,0.5,
|
| 1661 |
+
600.000 - 699.999 kr.,4.0,1.0,61,1.0,1.0,2801,3,0.25,1.0
|
| 1662 |
+
700.000 eller derover,3.0,1.0,48,1.0,1.0,2807,3,0.5,1.0
|
| 1663 |
+
Onsker ikke at oplyse,4.0,1.0,48,,1.0,2811,3,0.75,
|
| 1664 |
+
700.000 eller derover,5.0,0.0,51,1.0,1.0,2813,3,0.5,1.0
|
| 1665 |
+
200.000 - 299.999 kr.,2.0,1.0,70,,1.0,2818,3,0.75,
|
| 1666 |
+
400.000 - 499.999 kr.,4.0,1.0,52,,1.0,2824,3,0.75,
|
| 1667 |
+
100.000 - 199.999 kr.,5.0,1.0,58,1.0,1.0,2830,3,0.5,1.0
|
| 1668 |
+
500.000 - 599.999 kr.,5.0,1.0,43,,1.0,2845,3,0.75,
|
| 1669 |
+
Onsker ikke at oplyse,4.0,1.0,41,0.0,1.0,2847,3,0.5,0.0
|
| 1670 |
+
300.000 - 399.999 kr.,2.0,1.0,49,1.0,1.0,2848,3,0.75,1.0
|
| 1671 |
+
700.000 eller derover,5.0,1.0,52,1.0,1.0,2849,3,0.75,1.0
|
| 1672 |
+
600.000 - 699.999 kr.,2.0,1.0,52,,1.0,2851,3,0.25,
|
| 1673 |
+
300.000 - 399.999 kr.,4.0,1.0,42,0.0,1.0,2852,3,0.25,0.0
|
| 1674 |
+
700.000 eller derover,5.0,1.0,79,0.0,1.0,2853,3,0.25,0.0
|
| 1675 |
+
100.000 - 199.999 kr.,2.0,0.0,62,,1.0,2855,3,0.75,
|
| 1676 |
+
400.000 - 499.999 kr.,4.0,1.0,65,0.0,1.0,2856,3,0.75,0.0
|
| 1677 |
+
700.000 eller derover,4.0,0.0,37,0.0,1.0,2860,3,0.5,0.0
|
| 1678 |
+
Onsker ikke at oplyse,3.0,1.0,47,,1.0,2863,3,0.75,
|
| 1679 |
+
600.000 - 699.999 kr.,3.0,0.0,56,1.0,1.0,2864,3,0.25,1.0
|
| 1680 |
+
600.000 - 699.999 kr.,4.0,1.0,50,,1.0,2865,3,1.0,
|
| 1681 |
+
700.000 eller derover,5.0,1.0,42,,1.0,2869,3,0.75,
|
| 1682 |
+
400.000 - 499.999 kr.,1.0,1.0,59,0.0,1.0,2874,3,0.75,0.0
|
| 1683 |
+
500.000 - 599.999 kr.,5.0,1.0,72,1.0,1.0,2876,3,0.5,1.0
|
| 1684 |
+
700.000 eller derover,2.0,0.0,58,0.0,1.0,2879,3,0.75,0.0
|
| 1685 |
+
700.000 eller derover,3.0,1.0,54,,1.0,2880,3,0.5,
|
| 1686 |
+
700.000 eller derover,4.0,0.0,54,1.0,1.0,2883,3,0.5,1.0
|
| 1687 |
+
400.000 - 499.999 kr.,4.0,0.0,64,1.0,1.0,2884,3,0.75,1.0
|
| 1688 |
+
700.000 eller derover,2.0,1.0,49,1.0,1.0,2886,3,1.0,1.0
|
| 1689 |
+
600.000 - 699.999 kr.,4.0,0.0,51,1.0,1.0,2887,3,0.75,1.0
|
| 1690 |
+
700.000 eller derover,5.0,0.0,34,0.0,1.0,2890,3,0.25,0.0
|
| 1691 |
+
700.000 eller derover,4.0,1.0,53,0.0,1.0,2891,3,0.75,0.0
|
| 1692 |
+
700.000 eller derover,5.0,0.0,55,1.0,1.0,2892,3,0.75,1.0
|
| 1693 |
+
400.000 - 499.999 kr.,4.0,1.0,49,0.0,1.0,2895,3,0.75,0.0
|
| 1694 |
+
400.000 - 499.999 kr.,5.0,0.0,27,,1.0,2896,3,0.25,
|
| 1695 |
+
500.000 - 599.999 kr.,2.0,0.0,47,0.0,1.0,2900,3,1.0,0.0
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/data/metadata.txt
ADDED
|
@@ -0,0 +1,14 @@
|
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|
| 1 |
+
| Variable Name | Description |
|
| 2 |
+
|-----------------|--------------------------------------------------------------------------------------------------------------|
|
| 3 |
+
| income | Self-reported annual household income category in DKK (text categories; Danish labels). |
|
| 4 |
+
| educationClean | Highest completed education level (cleaned ordinal/numeric code). |
|
| 5 |
+
| sex | Respondent sex (binary code). |
|
| 6 |
+
| age | Respondent age in years. |
|
| 7 |
+
| partytimeinvar | Baseline party identification indicator (0 = identifies with incumbent Center-Right parties; 1 = opposition).|
|
| 8 |
+
| partycue | Period flag relative to May 2010 public messaging shift on the budget deficit (0 = before; 1 = after). |
|
| 9 |
+
| id | Anonymous respondent identifier. |
|
| 10 |
+
| time | Survey wave index (ordered interview period). |
|
| 11 |
+
| bi | Perceived seriousness of the national budget deficit, scaled 0–1 (higher values = more serious). |
|
| 12 |
+
| treatment | Indicator equal to 1 for government-identifying respondents observed after May 2010; 0 otherwise. |
|
| 13 |
+
|
| 14 |
+
Data Description: The dataset comes from a closely spaced five-wave Internet panel survey conducted in Denmark by Epinion in 2010–2011, targeting adults aged 18–65 and designed to track attitudes toward key economic conditions during and after the Great Recession. Respondents were interviewed in February, late March–early April, June 2010, January 2011, and June 2011, with demographics (income, education, sex, age) and baseline party identification collected alongside repeated measures of perceptions of the budget deficit. The panel spans a period that included the Danish government’s Restoration Act announcement (May 19, 2010), which received substantial media attention; the dataset therefore includes simple indicators for interview timing relative to this public messaging shift and a combined group-by-period flag. The study’s broader aim is to examine how messages from political parties relate to citizens’ interpretations of economic reality, focusing on perceptions of the public budget deficit in Denmark.
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/all_q.py
ADDED
|
@@ -0,0 +1,236 @@
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
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|
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|
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|
|
|
|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import sys
|
| 2 |
+
import json
|
| 3 |
+
import warnings
|
| 4 |
+
from pathlib import Path
|
| 5 |
+
|
| 6 |
+
import numpy as np
|
| 7 |
+
import pandas as pd
|
| 8 |
+
import statsmodels.formula.api as smf
|
| 9 |
+
|
| 10 |
+
warnings.filterwarnings("ignore")
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
def load_data(path):
|
| 14 |
+
df = pd.read_csv(path)
|
| 15 |
+
# Ensure expected columns exist
|
| 16 |
+
required_cols = ["id", "time", "bi", "treatment"]
|
| 17 |
+
for c in required_cols:
|
| 18 |
+
if c not in df.columns:
|
| 19 |
+
raise ValueError(f"Required column '{c}' not found in the dataset.")
|
| 20 |
+
# Coerce types
|
| 21 |
+
df["id"] = pd.to_numeric(df["id"], errors="coerce")
|
| 22 |
+
df["time"] = pd.to_numeric(df["time"], errors="coerce")
|
| 23 |
+
df["bi"] = pd.to_numeric(df["bi"], errors="coerce")
|
| 24 |
+
df["treatment"] = pd.to_numeric(df["treatment"], errors="coerce")
|
| 25 |
+
|
| 26 |
+
df = df.dropna(subset=["id", "time", "bi", "treatment"]).copy()
|
| 27 |
+
df["id"] = df["id"].astype(int)
|
| 28 |
+
df["time"] = df["time"].astype(int)
|
| 29 |
+
|
| 30 |
+
# Controls (if present)
|
| 31 |
+
controls = []
|
| 32 |
+
for c in ["age", "sex", "educationClean", "income"]:
|
| 33 |
+
if c in df.columns:
|
| 34 |
+
controls.append(c)
|
| 35 |
+
return df, controls
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
def prepare_groups(df):
|
| 39 |
+
# Ever-treated group indicator (treated group)
|
| 40 |
+
ever_treated = df.groupby("id")["treatment"].max().rename("treated_group")
|
| 41 |
+
df = df.merge(ever_treated, on="id", how="left")
|
| 42 |
+
df["treated_group"] = (df["treated_group"] > 0).astype(int)
|
| 43 |
+
return df
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
def find_candidate_pairs(df, max_questions=5):
|
| 47 |
+
times_sorted = sorted(df["time"].unique().tolist())
|
| 48 |
+
# Identify first post period with any treatment == 1
|
| 49 |
+
treated_by_time = df.groupby("time")["treatment"].sum()
|
| 50 |
+
post_times = [t for t in times_sorted if treated_by_time.get(t, 0) > 0]
|
| 51 |
+
pairs = []
|
| 52 |
+
|
| 53 |
+
if len(post_times) > 0:
|
| 54 |
+
t_post = min(post_times)
|
| 55 |
+
# Pre is latest time strictly less than t_post
|
| 56 |
+
pre_candidates = [t for t in times_sorted if t < t_post]
|
| 57 |
+
if len(pre_candidates) > 0:
|
| 58 |
+
t_pre = max(pre_candidates)
|
| 59 |
+
# Q1: Just before vs just after
|
| 60 |
+
pairs.append((t_pre, t_post))
|
| 61 |
+
# Q2: Earliest vs first post
|
| 62 |
+
t_first = times_sorted[0]
|
| 63 |
+
if t_first != t_pre:
|
| 64 |
+
pairs.append((t_first, t_post))
|
| 65 |
+
# Q3: Just before vs next post (if exists)
|
| 66 |
+
next_posts = [t for t in times_sorted if t > t_post]
|
| 67 |
+
if len(next_posts) > 0:
|
| 68 |
+
pairs.append((t_pre, next_posts[0]))
|
| 69 |
+
# Q4: Just before vs last observed time (if different)
|
| 70 |
+
t_last = times_sorted[-1]
|
| 71 |
+
if t_last not in [t_post, t_pre]:
|
| 72 |
+
pairs.append((t_pre, t_last))
|
| 73 |
+
# Q5: Placebo pre-pre if possible
|
| 74 |
+
earlier_pre = [t for t in pre_candidates if t < t_pre]
|
| 75 |
+
if len(earlier_pre) > 0:
|
| 76 |
+
pairs.append((earlier_pre[-1], t_pre))
|
| 77 |
+
else:
|
| 78 |
+
# Fallback: if no time has treatment==1, try consecutive pairs
|
| 79 |
+
for i in range(len(times_sorted) - 1):
|
| 80 |
+
pairs.append((times_sorted[i], times_sorted[i + 1]))
|
| 81 |
+
|
| 82 |
+
# Deduplicate and limit
|
| 83 |
+
uniq_pairs = []
|
| 84 |
+
for p in pairs:
|
| 85 |
+
if p not in uniq_pairs and p[0] != p[1]:
|
| 86 |
+
uniq_pairs.append(p)
|
| 87 |
+
return uniq_pairs[:max_questions]
|
| 88 |
+
|
| 89 |
+
|
| 90 |
+
def fit_2x2_did(df, pre_t, post_t, controls):
|
| 91 |
+
# Subset to the two periods
|
| 92 |
+
dsub = df[df["time"].isin([pre_t, post_t])].copy()
|
| 93 |
+
|
| 94 |
+
# Keep only units observed in both periods
|
| 95 |
+
counts = dsub.groupby("id")["time"].nunique()
|
| 96 |
+
keep_ids = counts[counts == 2].index
|
| 97 |
+
dsub = dsub[dsub["id"].isin(keep_ids)].copy()
|
| 98 |
+
|
| 99 |
+
# Must have both treated and control groups for identification
|
| 100 |
+
if dsub["treated_group"].nunique() < 2:
|
| 101 |
+
return None
|
| 102 |
+
|
| 103 |
+
# Define pair-specific post indicator and DID treatment interaction
|
| 104 |
+
dsub["post_pair"] = (dsub["time"] == post_t).astype(int)
|
| 105 |
+
dsub["D_pair"] = dsub["treated_group"] * dsub["post_pair"]
|
| 106 |
+
|
| 107 |
+
# Build formula: outcome ~ D_pair + treated_group + post_pair + controls (+ post_pair interactions)
|
| 108 |
+
base_terms = ["D_pair", "treated_group", "post_pair"]
|
| 109 |
+
control_terms = []
|
| 110 |
+
inter_terms = []
|
| 111 |
+
|
| 112 |
+
# Add controls only if they vary (at least two unique values and not all missing)
|
| 113 |
+
if "age" in controls and dsub["age"].notna().sum() > 0 and dsub["age"].nunique() > 1:
|
| 114 |
+
control_terms.append("age")
|
| 115 |
+
inter_terms.append("post_pair:age")
|
| 116 |
+
if "sex" in controls and dsub["sex"].notna().sum() > 0 and dsub["sex"].nunique() > 1:
|
| 117 |
+
control_terms.append("sex")
|
| 118 |
+
inter_terms.append("post_pair:sex")
|
| 119 |
+
if "educationClean" in controls and dsub["educationClean"].notna().sum() > 0 and dsub["educationClean"].nunique() > 1:
|
| 120 |
+
control_terms.append("educationClean")
|
| 121 |
+
inter_terms.append("post_pair:educationClean")
|
| 122 |
+
if "income" in controls and dsub["income"].notna().sum() > 0 and dsub["income"].nunique() > 1:
|
| 123 |
+
# treat income as categorical
|
| 124 |
+
control_terms.append("C(income)")
|
| 125 |
+
inter_terms.append("post_pair:C(income)")
|
| 126 |
+
|
| 127 |
+
rhs = base_terms + control_terms + inter_terms
|
| 128 |
+
formula = "bi ~ " + " + ".join(rhs)
|
| 129 |
+
|
| 130 |
+
# Fit OLS with cluster-robust SEs at id level
|
| 131 |
+
try:
|
| 132 |
+
model = smf.ols(formula=formula, data=dsub)
|
| 133 |
+
res = model.fit(cov_type="cluster", cov_kwds={"groups": dsub["id"]})
|
| 134 |
+
except Exception:
|
| 135 |
+
return None
|
| 136 |
+
|
| 137 |
+
if "D_pair" not in res.params.index:
|
| 138 |
+
return None
|
| 139 |
+
|
| 140 |
+
coef = res.params.get("D_pair", np.nan)
|
| 141 |
+
se = res.bse.get("D_pair", np.nan)
|
| 142 |
+
pval = res.pvalues.get("D_pair", np.nan)
|
| 143 |
+
|
| 144 |
+
if pd.isnull(coef) or pd.isnull(se):
|
| 145 |
+
return None
|
| 146 |
+
|
| 147 |
+
ci_low = coef - 1.96 * se
|
| 148 |
+
ci_high = coef + 1.96 * se
|
| 149 |
+
|
| 150 |
+
return {
|
| 151 |
+
"coef": float(coef),
|
| 152 |
+
"se": float(se),
|
| 153 |
+
"pval": None if pd.isnull(pval) else float(pval),
|
| 154 |
+
"ci": (float(ci_low), float(ci_high)),
|
| 155 |
+
"n_ids": int(dsub["id"].nunique()),
|
| 156 |
+
"n_obs": int(len(dsub)),
|
| 157 |
+
"used_controls": [c for c in ["age", "sex", "educationClean", "income"] if (("C(income)" if c == "income" else c) in control_terms)]
|
| 158 |
+
}
|
| 159 |
+
|
| 160 |
+
|
| 161 |
+
def make_json(question_idx, result, pre_t, post_t, controls, out_dir):
|
| 162 |
+
identification_strategy = {
|
| 163 |
+
"strategy": "Difference-in-Differences",
|
| 164 |
+
"variant": f"sharp 2x2 (pre={pre_t}, post={post_t}; ever-treated vs never-treated)",
|
| 165 |
+
"treatments": ["treatment"],
|
| 166 |
+
"outcomes": ["bi"],
|
| 167 |
+
"outcome_is_stacked": False,
|
| 168 |
+
"controls": result["used_controls"] if result["used_controls"] else None,
|
| 169 |
+
"post_treatment_variables": None,
|
| 170 |
+
"minimal_controlling_set": None,
|
| 171 |
+
"reason_for_minimal_controlling_set": None,
|
| 172 |
+
"time_variable": "time",
|
| 173 |
+
"group_variable": "id",
|
| 174 |
+
}
|
| 175 |
+
|
| 176 |
+
exact_q = (
|
| 177 |
+
f"ATT of treatment on bi using a 2x2 DiD with pre={pre_t} and post={post_t}, "
|
| 178 |
+
f"comparing ever-treated units (id with treatment=1 in any period) to never-treated units."
|
| 179 |
+
)
|
| 180 |
+
layman_q = (
|
| 181 |
+
f"How did the change between time {pre_t} and {post_t} affect the outcome for treated "
|
| 182 |
+
f"individuals compared to untreated individuals?"
|
| 183 |
+
)
|
| 184 |
+
|
| 185 |
+
payload = {
|
| 186 |
+
"identification_strategy": identification_strategy,
|
| 187 |
+
"quantity": "ATT",
|
| 188 |
+
"estimand_population": "treated (ever-treated units observed in both periods)",
|
| 189 |
+
"quantity_value": result["coef"],
|
| 190 |
+
"quantity_ci": {
|
| 191 |
+
"lower": result["ci"][0],
|
| 192 |
+
"upper": result["ci"][1],
|
| 193 |
+
"level": 0.95,
|
| 194 |
+
},
|
| 195 |
+
"standard_error": result["se"],
|
| 196 |
+
"p_value": result["pval"],
|
| 197 |
+
"effect_units": "units of bi (0-1 scale)",
|
| 198 |
+
"subgroup": None,
|
| 199 |
+
"exact_causal_question": exact_q,
|
| 200 |
+
"layman_query": layman_q,
|
| 201 |
+
}
|
| 202 |
+
|
| 203 |
+
out_path = Path(out_dir) / f"question_{question_idx}.json"
|
| 204 |
+
with open(out_path, "w") as f:
|
| 205 |
+
json.dump(payload, f, indent=2)
|
| 206 |
+
|
| 207 |
+
|
| 208 |
+
def main():
|
| 209 |
+
if len(sys.argv) < 2:
|
| 210 |
+
raise SystemExit("Usage: python generate_causal_questions.py data.csv")
|
| 211 |
+
data_path = sys.argv[1]
|
| 212 |
+
out_dir = Path(".")
|
| 213 |
+
df, controls = load_data(data_path)
|
| 214 |
+
df = prepare_groups(df)
|
| 215 |
+
pairs = find_candidate_pairs(df, max_questions=5)
|
| 216 |
+
|
| 217 |
+
if len(pairs) == 0:
|
| 218 |
+
raise SystemExit("Could not identify suitable pre/post time pairs for 2x2 DiD.")
|
| 219 |
+
|
| 220 |
+
q_idx = 1
|
| 221 |
+
for (pre_t, post_t) in pairs:
|
| 222 |
+
try:
|
| 223 |
+
res = fit_2x2_did(df, pre_t, post_t, controls)
|
| 224 |
+
if res is None:
|
| 225 |
+
continue
|
| 226 |
+
make_json(q_idx, res, pre_t, post_t, controls, out_dir)
|
| 227 |
+
q_idx += 1
|
| 228 |
+
except Exception:
|
| 229 |
+
continue
|
| 230 |
+
|
| 231 |
+
if q_idx == 1:
|
| 232 |
+
raise SystemExit("No valid 2x2 DiD questions could be estimated.")
|
| 233 |
+
|
| 234 |
+
|
| 235 |
+
if __name__ == "__main__":
|
| 236 |
+
main()
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/all_q_dml.py
ADDED
|
@@ -0,0 +1,286 @@
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|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import sys
|
| 2 |
+
import json
|
| 3 |
+
import warnings
|
| 4 |
+
from pathlib import Path
|
| 5 |
+
|
| 6 |
+
import numpy as np
|
| 7 |
+
import pandas as pd
|
| 8 |
+
import statsmodels.formula.api as smf
|
| 9 |
+
import doubleml as dml
|
| 10 |
+
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
|
| 11 |
+
|
| 12 |
+
warnings.filterwarnings("ignore")
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
def load_data(path):
|
| 16 |
+
df = pd.read_csv(path)
|
| 17 |
+
# Ensure expected columns exist
|
| 18 |
+
required_cols = ["id", "time", "bi", "treatment"]
|
| 19 |
+
for c in required_cols:
|
| 20 |
+
if c not in df.columns:
|
| 21 |
+
raise ValueError(f"Required column '{c}' not found in the dataset.")
|
| 22 |
+
# Coerce types
|
| 23 |
+
df["id"] = pd.to_numeric(df["id"], errors="coerce")
|
| 24 |
+
df["time"] = pd.to_numeric(df["time"], errors="coerce")
|
| 25 |
+
df["bi"] = pd.to_numeric(df["bi"], errors="coerce")
|
| 26 |
+
df["treatment"] = pd.to_numeric(df["treatment"], errors="coerce")
|
| 27 |
+
|
| 28 |
+
df = df.dropna(subset=["id", "time", "bi", "treatment"]).copy()
|
| 29 |
+
df["id"] = df["id"].astype(int)
|
| 30 |
+
df["time"] = df["time"].astype(int)
|
| 31 |
+
|
| 32 |
+
# Controls (if present)
|
| 33 |
+
controls = []
|
| 34 |
+
for c in ["age", "sex", "educationClean", "income"]:
|
| 35 |
+
if c in df.columns:
|
| 36 |
+
controls.append(c)
|
| 37 |
+
return df, controls
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
def prepare_groups(df):
|
| 41 |
+
# Ever-treated group indicator (treated group)
|
| 42 |
+
ever_treated = df.groupby("id")["treatment"].max().rename("treated_group")
|
| 43 |
+
df = df.merge(ever_treated, on="id", how="left")
|
| 44 |
+
df["treated_group"] = (df["treated_group"] > 0).astype(int)
|
| 45 |
+
return df
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
def find_candidate_pairs(df, max_questions=5):
|
| 49 |
+
times_sorted = sorted(df["time"].unique().tolist())
|
| 50 |
+
# Identify first post period with any treatment == 1
|
| 51 |
+
treated_by_time = df.groupby("time")["treatment"].sum()
|
| 52 |
+
post_times = [t for t in times_sorted if treated_by_time.get(t, 0) > 0]
|
| 53 |
+
pairs = []
|
| 54 |
+
|
| 55 |
+
if len(post_times) > 0:
|
| 56 |
+
t_post = min(post_times)
|
| 57 |
+
# Pre is latest time strictly less than t_post
|
| 58 |
+
pre_candidates = [t for t in times_sorted if t < t_post]
|
| 59 |
+
if len(pre_candidates) > 0:
|
| 60 |
+
t_pre = max(pre_candidates)
|
| 61 |
+
# Q1: Just before vs just after
|
| 62 |
+
pairs.append((t_pre, t_post))
|
| 63 |
+
# Q2: Earliest vs first post
|
| 64 |
+
t_first = times_sorted[0]
|
| 65 |
+
if t_first != t_pre:
|
| 66 |
+
pairs.append((t_first, t_post))
|
| 67 |
+
# Q3: Just before vs next post (if exists)
|
| 68 |
+
next_posts = [t for t in times_sorted if t > t_post]
|
| 69 |
+
if len(next_posts) > 0:
|
| 70 |
+
pairs.append((t_pre, next_posts[0]))
|
| 71 |
+
# Q4: Just before vs last observed time (if different)
|
| 72 |
+
t_last = times_sorted[-1]
|
| 73 |
+
if t_last not in [t_post, t_pre]:
|
| 74 |
+
pairs.append((t_pre, t_last))
|
| 75 |
+
# Q5: Placebo pre-pre if possible
|
| 76 |
+
earlier_pre = [t for t in pre_candidates if t < t_pre]
|
| 77 |
+
if len(earlier_pre) > 0:
|
| 78 |
+
pairs.append((earlier_pre[-1], t_pre))
|
| 79 |
+
else:
|
| 80 |
+
# Fallback: if no time has treatment==1, try consecutive pairs
|
| 81 |
+
for i in range(len(times_sorted) - 1):
|
| 82 |
+
pairs.append((times_sorted[i], times_sorted[i + 1]))
|
| 83 |
+
|
| 84 |
+
# Deduplicate and limit
|
| 85 |
+
uniq_pairs = []
|
| 86 |
+
for p in pairs:
|
| 87 |
+
if p not in uniq_pairs and p[0] != p[1]:
|
| 88 |
+
uniq_pairs.append(p)
|
| 89 |
+
return uniq_pairs[:max_questions]
|
| 90 |
+
|
| 91 |
+
|
| 92 |
+
def fit_2x2_did(df, pre_t, post_t, controls):
|
| 93 |
+
# Subset to the two periods
|
| 94 |
+
dsub = df[df["time"].isin([pre_t, post_t])].copy()
|
| 95 |
+
|
| 96 |
+
# Keep only units observed in both periods
|
| 97 |
+
counts = dsub.groupby("id")["time"].nunique()
|
| 98 |
+
keep_ids = counts[counts == 2].index
|
| 99 |
+
dsub = dsub[dsub["id"].isin(keep_ids)].copy()
|
| 100 |
+
|
| 101 |
+
# Must have both treated and control groups for identification
|
| 102 |
+
if dsub["treated_group"].nunique() < 2:
|
| 103 |
+
return None
|
| 104 |
+
|
| 105 |
+
# Define pair-specific post indicator
|
| 106 |
+
dsub["post_pair"] = (dsub["time"] == post_t).astype(int)
|
| 107 |
+
|
| 108 |
+
# Build control terms (only for reporting used_controls; mirrors original logic)
|
| 109 |
+
control_terms = []
|
| 110 |
+
inter_terms = []
|
| 111 |
+
|
| 112 |
+
if "age" in controls and dsub["age"].notna().sum() > 0 and dsub["age"].nunique() > 1:
|
| 113 |
+
control_terms.append("age")
|
| 114 |
+
inter_terms.append("post_pair:age")
|
| 115 |
+
if "sex" in controls and dsub["sex"].notna().sum() > 0 and dsub["sex"].nunique() > 1:
|
| 116 |
+
control_terms.append("sex")
|
| 117 |
+
inter_terms.append("post_pair:sex")
|
| 118 |
+
if "educationClean" in controls and dsub["educationClean"].notna().sum() > 0 and dsub["educationClean"].nunique() > 1:
|
| 119 |
+
control_terms.append("educationClean")
|
| 120 |
+
inter_terms.append("post_pair:educationClean")
|
| 121 |
+
if "income" in controls and dsub["income"].notna().sum() > 0 and dsub["income"].nunique() > 1:
|
| 122 |
+
# treat income as categorical in reporting
|
| 123 |
+
control_terms.append("C(income)")
|
| 124 |
+
inter_terms.append("post_pair:C(income)")
|
| 125 |
+
|
| 126 |
+
# Prepare wide data (one obs per id with delta outcome)
|
| 127 |
+
pre = dsub[dsub["time"] == pre_t].set_index("id")
|
| 128 |
+
post = dsub[dsub["time"] == post_t].set_index("id")
|
| 129 |
+
common_ids = pre.index.intersection(post.index)
|
| 130 |
+
if len(common_ids) < 2:
|
| 131 |
+
return None
|
| 132 |
+
|
| 133 |
+
pre = pre.loc[common_ids]
|
| 134 |
+
post = post.loc[common_ids]
|
| 135 |
+
|
| 136 |
+
# Outcome difference
|
| 137 |
+
y_diff = post["bi"] - pre["bi"]
|
| 138 |
+
# Treatment group (ever-treated)
|
| 139 |
+
d_vec = pre["treated_group"].astype(int)
|
| 140 |
+
|
| 141 |
+
# Features for DoubleML (flexible functions of pre levels and changes)
|
| 142 |
+
X = pd.DataFrame(index=common_ids)
|
| 143 |
+
|
| 144 |
+
if "age" in control_terms and "age" in pre.columns and "age" in post.columns:
|
| 145 |
+
X["age_pre"] = pre["age"]
|
| 146 |
+
X["age_change"] = post["age"] - pre["age"]
|
| 147 |
+
if "sex" in control_terms and "sex" in pre.columns and "sex" in post.columns:
|
| 148 |
+
X["sex_pre"] = pre["sex"]
|
| 149 |
+
X["sex_change"] = post["sex"] - pre["sex"]
|
| 150 |
+
if "educationClean" in control_terms and "educationClean" in pre.columns and "educationClean" in post.columns:
|
| 151 |
+
X["educationClean_pre"] = pre["educationClean"]
|
| 152 |
+
X["educationClean_change"] = post["educationClean"] - pre["educationClean"]
|
| 153 |
+
if "C(income)" in control_terms and "income" in pre.columns and "income" in post.columns:
|
| 154 |
+
# Encode income categorically via shared categories
|
| 155 |
+
all_cats = pd.Index(pd.concat([pre["income"], post["income"]], axis=0).astype(str)).unique()
|
| 156 |
+
inc_pre_codes = pd.Categorical(pre["income"].astype(str), categories=all_cats).codes
|
| 157 |
+
inc_post_codes = pd.Categorical(post["income"].astype(str), categories=all_cats).codes
|
| 158 |
+
X["income_pre_code"] = inc_pre_codes
|
| 159 |
+
X["income_change_code"] = inc_post_codes - inc_pre_codes
|
| 160 |
+
|
| 161 |
+
# If no controls selected or all missing, add a constant feature
|
| 162 |
+
if X.shape[1] == 0:
|
| 163 |
+
X["const"] = 1.0
|
| 164 |
+
|
| 165 |
+
# Assemble DoubleML dataset
|
| 166 |
+
df_dml = pd.DataFrame({"y": y_diff, "d": d_vec}).join(X)
|
| 167 |
+
df_dml = df_dml.replace([np.inf, -np.inf], np.nan).dropna(axis=0, how="any")
|
| 168 |
+
|
| 169 |
+
# Ensure both treatment groups remain
|
| 170 |
+
if df_dml["d"].nunique() < 2 or df_dml.shape[0] < 2:
|
| 171 |
+
return None
|
| 172 |
+
|
| 173 |
+
x_cols = [c for c in df_dml.columns if c not in ["y", "d"]]
|
| 174 |
+
if len(x_cols) == 0:
|
| 175 |
+
df_dml["const"] = 1.0
|
| 176 |
+
x_cols = ["const"]
|
| 177 |
+
|
| 178 |
+
try:
|
| 179 |
+
dml_data = dml.DoubleMLData(df_dml, y_col="y", x_cols=x_cols, d_cols="d")
|
| 180 |
+
ml_g = RandomForestRegressor(random_state=42)
|
| 181 |
+
ml_m = RandomForestClassifier(random_state=42)
|
| 182 |
+
dml_did = dml.DoubleMLDID(dml_data, ml_g=ml_g, ml_m=ml_m, score="observational")
|
| 183 |
+
dml_did.fit()
|
| 184 |
+
except Exception:
|
| 185 |
+
return None
|
| 186 |
+
|
| 187 |
+
try:
|
| 188 |
+
coef = float(np.atleast_1d(dml_did.coef).ravel()[0])
|
| 189 |
+
except Exception:
|
| 190 |
+
return None
|
| 191 |
+
se = float(np.atleast_1d(dml_did.se).ravel()[0]) if hasattr(dml_did, "se") else np.nan
|
| 192 |
+
pval = float(np.atleast_1d(dml_did.pval).ravel()[0]) if hasattr(dml_did, "pval") else None
|
| 193 |
+
|
| 194 |
+
if pd.isnull(coef) or pd.isnull(se):
|
| 195 |
+
return None
|
| 196 |
+
|
| 197 |
+
ci_low = coef - 1.96 * se
|
| 198 |
+
ci_high = coef + 1.96 * se
|
| 199 |
+
|
| 200 |
+
return {
|
| 201 |
+
"coef": float(coef),
|
| 202 |
+
"se": float(se),
|
| 203 |
+
"pval": None if pval is None or pd.isnull(pval) else float(pval),
|
| 204 |
+
"ci": (float(ci_low), float(ci_high)),
|
| 205 |
+
"n_ids": int(df_dml.shape[0]),
|
| 206 |
+
"n_obs": int(df_dml.shape[0] * 2),
|
| 207 |
+
"used_controls": [c for c in ["age", "sex", "educationClean", "income"] if (("C(income)" if c == "income" else c) in control_terms)]
|
| 208 |
+
}
|
| 209 |
+
|
| 210 |
+
|
| 211 |
+
def make_json(question_idx, result, pre_t, post_t, controls, out_dir):
|
| 212 |
+
identification_strategy = {
|
| 213 |
+
"strategy": "Difference-in-Differences",
|
| 214 |
+
"variant": f"sharp 2x2 (pre={pre_t}, post={post_t}; ever-treated vs never-treated)",
|
| 215 |
+
"treatments": ["treatment"],
|
| 216 |
+
"outcomes": ["bi"],
|
| 217 |
+
"outcome_is_stacked": False,
|
| 218 |
+
"controls": result["used_controls"] if result["used_controls"] else None,
|
| 219 |
+
"post_treatment_variables": None,
|
| 220 |
+
"minimal_controlling_set": None,
|
| 221 |
+
"reason_for_minimal_controlling_set": None,
|
| 222 |
+
"time_variable": "time",
|
| 223 |
+
"group_variable": "id",
|
| 224 |
+
}
|
| 225 |
+
|
| 226 |
+
exact_q = (
|
| 227 |
+
f"ATT of treatment on bi using a 2x2 DiD with pre={pre_t} and post={post_t}, "
|
| 228 |
+
f"comparing ever-treated units (id with treatment=1 in any period) to never-treated units."
|
| 229 |
+
)
|
| 230 |
+
layman_q = (
|
| 231 |
+
f"How did the change between time {pre_t} and {post_t} affect the outcome for treated "
|
| 232 |
+
f"individuals compared to untreated individuals?"
|
| 233 |
+
)
|
| 234 |
+
|
| 235 |
+
payload = {
|
| 236 |
+
"identification_strategy": identification_strategy,
|
| 237 |
+
"quantity": "ATT",
|
| 238 |
+
"estimand_population": "treated (ever-treated units observed in both periods)",
|
| 239 |
+
"quantity_value": result["coef"],
|
| 240 |
+
"quantity_ci": {
|
| 241 |
+
"lower": result["ci"][0],
|
| 242 |
+
"upper": result["ci"][1],
|
| 243 |
+
"level": 0.95,
|
| 244 |
+
},
|
| 245 |
+
"standard_error": result["se"],
|
| 246 |
+
"p_value": result["pval"],
|
| 247 |
+
"effect_units": "units of bi (0-1 scale)",
|
| 248 |
+
"subgroup": None,
|
| 249 |
+
"exact_causal_question": exact_q,
|
| 250 |
+
"layman_query": layman_q,
|
| 251 |
+
}
|
| 252 |
+
|
| 253 |
+
out_path = Path(out_dir) / f"question_{question_idx}_double_ml.json"
|
| 254 |
+
with open(out_path, "w") as f:
|
| 255 |
+
json.dump(payload, f, indent=2)
|
| 256 |
+
|
| 257 |
+
|
| 258 |
+
def main():
|
| 259 |
+
if len(sys.argv) < 2:
|
| 260 |
+
raise SystemExit("Usage: python generate_causal_questions.py data.csv")
|
| 261 |
+
data_path = sys.argv[1]
|
| 262 |
+
out_dir = Path(".")
|
| 263 |
+
df, controls = load_data(data_path)
|
| 264 |
+
df = prepare_groups(df)
|
| 265 |
+
pairs = find_candidate_pairs(df, max_questions=5)
|
| 266 |
+
|
| 267 |
+
if len(pairs) == 0:
|
| 268 |
+
raise SystemExit("Could not identify suitable pre/post time pairs for 2x2 DiD.")
|
| 269 |
+
|
| 270 |
+
q_idx = 1
|
| 271 |
+
for (pre_t, post_t) in pairs:
|
| 272 |
+
try:
|
| 273 |
+
res = fit_2x2_did(df, pre_t, post_t, controls)
|
| 274 |
+
if res is None:
|
| 275 |
+
continue
|
| 276 |
+
make_json(q_idx, res, pre_t, post_t, controls, out_dir)
|
| 277 |
+
q_idx += 1
|
| 278 |
+
except Exception:
|
| 279 |
+
continue
|
| 280 |
+
|
| 281 |
+
if q_idx == 1:
|
| 282 |
+
raise SystemExit("No valid 2x2 DiD questions could be estimated.")
|
| 283 |
+
|
| 284 |
+
|
| 285 |
+
if __name__ == "__main__":
|
| 286 |
+
main()
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/estimation_1.py
ADDED
|
@@ -0,0 +1,116 @@
|
|
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|
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|
|
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|
|
|
|
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|
|
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|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import sys
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
import numpy as np
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import statsmodels.formula.api as smf
|
| 6 |
+
|
| 7 |
+
# This script runs the 1st 2x2 DiD estimation (first candidate pair)
|
| 8 |
+
TARGET_INDEX = 0 # zero-based
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
def load_data(path):
|
| 12 |
+
df = pd.read_csv(path)
|
| 13 |
+
required = ["id", "time", "bi", "treatment"]
|
| 14 |
+
for c in required:
|
| 15 |
+
if c not in df.columns:
|
| 16 |
+
raise ValueError(f"Required column '{c}' not found")
|
| 17 |
+
df["id"] = pd.to_numeric(df["id"], errors="coerce")
|
| 18 |
+
df["time"] = pd.to_numeric(df["time"], errors="coerce")
|
| 19 |
+
df["bi"] = pd.to_numeric(df["bi"], errors="coerce")
|
| 20 |
+
df["treatment"] = pd.to_numeric(df["treatment"], errors="coerce")
|
| 21 |
+
df = df.dropna(subset=["id", "time", "bi", "treatment"]).copy()
|
| 22 |
+
df["id"] = df["id"].astype(int)
|
| 23 |
+
df["time"] = df["time"].astype(int)
|
| 24 |
+
controls = [c for c in ["age", "sex", "educationClean", "income"] if c in df.columns]
|
| 25 |
+
return df, controls
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
def prepare_groups(df):
|
| 29 |
+
ever = df.groupby("id")["treatment"].max().rename("treated_group")
|
| 30 |
+
df = df.merge(ever, on="id", how="left")
|
| 31 |
+
df["treated_group"] = (df["treated_group"] > 0).astype(int)
|
| 32 |
+
return df
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
def find_candidate_pairs(df, max_questions=5):
|
| 36 |
+
times_sorted = sorted(df["time"].unique().tolist())
|
| 37 |
+
treated_by_time = df.groupby("time")["treatment"].sum()
|
| 38 |
+
post_times = [t for t in times_sorted if treated_by_time.get(t, 0) > 0]
|
| 39 |
+
pairs = []
|
| 40 |
+
if len(post_times) > 0:
|
| 41 |
+
t_post = min(post_times)
|
| 42 |
+
pre_candidates = [t for t in times_sorted if t < t_post]
|
| 43 |
+
if len(pre_candidates) > 0:
|
| 44 |
+
t_pre = max(pre_candidates)
|
| 45 |
+
pairs.append((t_pre, t_post))
|
| 46 |
+
t_first = times_sorted[0]
|
| 47 |
+
if t_first != t_pre:
|
| 48 |
+
pairs.append((t_first, t_post))
|
| 49 |
+
next_posts = [t for t in times_sorted if t > t_post]
|
| 50 |
+
if len(next_posts) > 0:
|
| 51 |
+
pairs.append((t_pre, next_posts[0]))
|
| 52 |
+
t_last = times_sorted[-1]
|
| 53 |
+
if t_last not in [t_post, t_pre]:
|
| 54 |
+
pairs.append((t_pre, t_last))
|
| 55 |
+
earlier_pre = [t for t in pre_candidates if t < t_pre]
|
| 56 |
+
if len(earlier_pre) > 0:
|
| 57 |
+
pairs.append((earlier_pre[-1], t_pre))
|
| 58 |
+
else:
|
| 59 |
+
for i in range(len(times_sorted) - 1):
|
| 60 |
+
pairs.append((times_sorted[i], times_sorted[i + 1]))
|
| 61 |
+
uniq = []
|
| 62 |
+
for p in pairs:
|
| 63 |
+
if p not in uniq and p[0] != p[1]:
|
| 64 |
+
uniq.append(p)
|
| 65 |
+
return uniq[:max_questions]
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
def fit_2x2_did(df, pre_t, post_t, controls):
|
| 69 |
+
dsub = df[df["time"].isin([pre_t, post_t])].copy()
|
| 70 |
+
counts = dsub.groupby("id")["time"].nunique()
|
| 71 |
+
keep = counts[counts == 2].index
|
| 72 |
+
dsub = dsub[dsub["id"].isin(keep)].copy()
|
| 73 |
+
if dsub["treated_group"].nunique() < 2:
|
| 74 |
+
return None
|
| 75 |
+
dsub["post_pair"] = (dsub["time"] == post_t).astype(int)
|
| 76 |
+
dsub["D_pair"] = dsub["treated_group"] * dsub["post_pair"]
|
| 77 |
+
base = ["D_pair", "treated_group", "post_pair"]
|
| 78 |
+
control_terms = []
|
| 79 |
+
inter = []
|
| 80 |
+
if "age" in controls and dsub["age"].notna().sum() > 0 and dsub["age"].nunique() > 1:
|
| 81 |
+
control_terms.append("age"); inter.append("post_pair:age")
|
| 82 |
+
if "sex" in controls and dsub["sex"].notna().sum() > 0 and dsub["sex"].nunique() > 1:
|
| 83 |
+
control_terms.append("sex"); inter.append("post_pair:sex")
|
| 84 |
+
if "educationClean" in controls and dsub["educationClean"].notna().sum() > 0 and dsub["educationClean"].nunique() > 1:
|
| 85 |
+
control_terms.append("educationClean"); inter.append("post_pair:educationClean")
|
| 86 |
+
if "income" in controls and dsub["income"].notna().sum() > 0 and dsub["income"].nunique() > 1:
|
| 87 |
+
control_terms.append("C(income)"); inter.append("post_pair:C(income)")
|
| 88 |
+
rhs = base + control_terms + inter
|
| 89 |
+
formula = "bi ~ " + " + ".join(rhs)
|
| 90 |
+
try:
|
| 91 |
+
res = smf.ols(formula=formula, data=dsub).fit(cov_type="cluster", cov_kwds={"groups": dsub["id"]})
|
| 92 |
+
except Exception:
|
| 93 |
+
return None
|
| 94 |
+
if "D_pair" not in res.params.index:
|
| 95 |
+
return None
|
| 96 |
+
coef = res.params.get("D_pair", np.nan)
|
| 97 |
+
se = res.bse.get("D_pair", np.nan)
|
| 98 |
+
return {"coef": float(coef), "se": float(se)}
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
if __name__ == "__main__":
|
| 102 |
+
if len(sys.argv) < 2:
|
| 103 |
+
raise SystemExit("Usage: python estimation_1.py data.csv")
|
| 104 |
+
path = sys.argv[1]
|
| 105 |
+
df, controls = load_data(path)
|
| 106 |
+
df = prepare_groups(df)
|
| 107 |
+
pairs = find_candidate_pairs(df, max_questions=5)
|
| 108 |
+
if len(pairs) <= TARGET_INDEX:
|
| 109 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 110 |
+
raise SystemExit(0)
|
| 111 |
+
pre_t, post_t = pairs[TARGET_INDEX]
|
| 112 |
+
res = fit_2x2_did(df, pre_t, post_t, controls)
|
| 113 |
+
if res is None:
|
| 114 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 115 |
+
else:
|
| 116 |
+
print(f"effect: {res['coef']} and std_error: {res['se']}")
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/estimation_2.py
ADDED
|
@@ -0,0 +1,116 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import sys
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
import numpy as np
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import statsmodels.formula.api as smf
|
| 6 |
+
|
| 7 |
+
# This script runs the 2nd 2x2 DiD estimation (second candidate pair)
|
| 8 |
+
TARGET_INDEX = 1 # zero-based
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
def load_data(path):
|
| 12 |
+
df = pd.read_csv(path)
|
| 13 |
+
required = ["id", "time", "bi", "treatment"]
|
| 14 |
+
for c in required:
|
| 15 |
+
if c not in df.columns:
|
| 16 |
+
raise ValueError(f"Required column '{c}' not found")
|
| 17 |
+
df["id"] = pd.to_numeric(df["id"], errors="coerce")
|
| 18 |
+
df["time"] = pd.to_numeric(df["time"], errors="coerce")
|
| 19 |
+
df["bi"] = pd.to_numeric(df["bi"], errors="coerce")
|
| 20 |
+
df["treatment"] = pd.to_numeric(df["treatment"], errors="coerce")
|
| 21 |
+
df = df.dropna(subset=["id", "time", "bi", "treatment"]).copy()
|
| 22 |
+
df["id"] = df["id"].astype(int)
|
| 23 |
+
df["time"] = df["time"].astype(int)
|
| 24 |
+
controls = [c for c in ["age", "sex", "educationClean", "income"] if c in df.columns]
|
| 25 |
+
return df, controls
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
def prepare_groups(df):
|
| 29 |
+
ever = df.groupby("id")["treatment"].max().rename("treated_group")
|
| 30 |
+
df = df.merge(ever, on="id", how="left")
|
| 31 |
+
df["treated_group"] = (df["treated_group"] > 0).astype(int)
|
| 32 |
+
return df
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
def find_candidate_pairs(df, max_questions=5):
|
| 36 |
+
times_sorted = sorted(df["time"].unique().tolist())
|
| 37 |
+
treated_by_time = df.groupby("time")["treatment"].sum()
|
| 38 |
+
post_times = [t for t in times_sorted if treated_by_time.get(t, 0) > 0]
|
| 39 |
+
pairs = []
|
| 40 |
+
if len(post_times) > 0:
|
| 41 |
+
t_post = min(post_times)
|
| 42 |
+
pre_candidates = [t for t in times_sorted if t < t_post]
|
| 43 |
+
if len(pre_candidates) > 0:
|
| 44 |
+
t_pre = max(pre_candidates)
|
| 45 |
+
pairs.append((t_pre, t_post))
|
| 46 |
+
t_first = times_sorted[0]
|
| 47 |
+
if t_first != t_pre:
|
| 48 |
+
pairs.append((t_first, t_post))
|
| 49 |
+
next_posts = [t for t in times_sorted if t > t_post]
|
| 50 |
+
if len(next_posts) > 0:
|
| 51 |
+
pairs.append((t_pre, next_posts[0]))
|
| 52 |
+
t_last = times_sorted[-1]
|
| 53 |
+
if t_last not in [t_post, t_pre]:
|
| 54 |
+
pairs.append((t_pre, t_last))
|
| 55 |
+
earlier_pre = [t for t in pre_candidates if t < t_pre]
|
| 56 |
+
if len(earlier_pre) > 0:
|
| 57 |
+
pairs.append((earlier_pre[-1], t_pre))
|
| 58 |
+
else:
|
| 59 |
+
for i in range(len(times_sorted) - 1):
|
| 60 |
+
pairs.append((times_sorted[i], times_sorted[i + 1]))
|
| 61 |
+
uniq = []
|
| 62 |
+
for p in pairs:
|
| 63 |
+
if p not in uniq and p[0] != p[1]:
|
| 64 |
+
uniq.append(p)
|
| 65 |
+
return uniq[:max_questions]
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
def fit_2x2_did(df, pre_t, post_t, controls):
|
| 69 |
+
dsub = df[df["time"].isin([pre_t, post_t])].copy()
|
| 70 |
+
counts = dsub.groupby("id")["time"].nunique()
|
| 71 |
+
keep = counts[counts == 2].index
|
| 72 |
+
dsub = dsub[dsub["id"].isin(keep)].copy()
|
| 73 |
+
if dsub["treated_group"].nunique() < 2:
|
| 74 |
+
return None
|
| 75 |
+
dsub["post_pair"] = (dsub["time"] == post_t).astype(int)
|
| 76 |
+
dsub["D_pair"] = dsub["treated_group"] * dsub["post_pair"]
|
| 77 |
+
base = ["D_pair", "treated_group", "post_pair"]
|
| 78 |
+
control_terms = []
|
| 79 |
+
inter = []
|
| 80 |
+
if "age" in controls and dsub["age"].notna().sum() > 0 and dsub["age"].nunique() > 1:
|
| 81 |
+
control_terms.append("age"); inter.append("post_pair:age")
|
| 82 |
+
if "sex" in controls and dsub["sex"].notna().sum() > 0 and dsub["sex"].nunique() > 1:
|
| 83 |
+
control_terms.append("sex"); inter.append("post_pair:sex")
|
| 84 |
+
if "educationClean" in controls and dsub["educationClean"].notna().sum() > 0 and dsub["educationClean"].nunique() > 1:
|
| 85 |
+
control_terms.append("educationClean"); inter.append("post_pair:educationClean")
|
| 86 |
+
if "income" in controls and dsub["income"].notna().sum() > 0 and dsub["income"].nunique() > 1:
|
| 87 |
+
control_terms.append("C(income)"); inter.append("post_pair:C(income)")
|
| 88 |
+
rhs = base + control_terms + inter
|
| 89 |
+
formula = "bi ~ " + " + ".join(rhs)
|
| 90 |
+
try:
|
| 91 |
+
res = smf.ols(formula=formula, data=dsub).fit(cov_type="cluster", cov_kwds={"groups": dsub["id"]})
|
| 92 |
+
except Exception:
|
| 93 |
+
return None
|
| 94 |
+
if "D_pair" not in res.params.index:
|
| 95 |
+
return None
|
| 96 |
+
coef = res.params.get("D_pair", np.nan)
|
| 97 |
+
se = res.bse.get("D_pair", np.nan)
|
| 98 |
+
return {"coef": float(coef), "se": float(se)}
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
if __name__ == "__main__":
|
| 102 |
+
if len(sys.argv) < 2:
|
| 103 |
+
raise SystemExit("Usage: python estimation_2.py data.csv")
|
| 104 |
+
path = sys.argv[1]
|
| 105 |
+
df, controls = load_data(path)
|
| 106 |
+
df = prepare_groups(df)
|
| 107 |
+
pairs = find_candidate_pairs(df, max_questions=5)
|
| 108 |
+
if len(pairs) <= TARGET_INDEX:
|
| 109 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 110 |
+
raise SystemExit(0)
|
| 111 |
+
pre_t, post_t = pairs[TARGET_INDEX]
|
| 112 |
+
res = fit_2x2_did(df, pre_t, post_t, controls)
|
| 113 |
+
if res is None:
|
| 114 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 115 |
+
else:
|
| 116 |
+
print(f"effect: {res['coef']} and std_error: {res['se']}")
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/estimation_3.py
ADDED
|
@@ -0,0 +1,116 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import sys
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
import numpy as np
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import statsmodels.formula.api as smf
|
| 6 |
+
|
| 7 |
+
# This script runs the 3rd 2x2 DiD estimation (third candidate pair)
|
| 8 |
+
TARGET_INDEX = 2 # zero-based
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
def load_data(path):
|
| 12 |
+
df = pd.read_csv(path)
|
| 13 |
+
required = ["id", "time", "bi", "treatment"]
|
| 14 |
+
for c in required:
|
| 15 |
+
if c not in df.columns:
|
| 16 |
+
raise ValueError(f"Required column '{c}' not found")
|
| 17 |
+
df["id"] = pd.to_numeric(df["id"], errors="coerce")
|
| 18 |
+
df["time"] = pd.to_numeric(df["time"], errors="coerce")
|
| 19 |
+
df["bi"] = pd.to_numeric(df["bi"], errors="coerce")
|
| 20 |
+
df["treatment"] = pd.to_numeric(df["treatment"], errors="coerce")
|
| 21 |
+
df = df.dropna(subset=["id", "time", "bi", "treatment"]).copy()
|
| 22 |
+
df["id"] = df["id"].astype(int)
|
| 23 |
+
df["time"] = df["time"].astype(int)
|
| 24 |
+
controls = [c for c in ["age", "sex", "educationClean", "income"] if c in df.columns]
|
| 25 |
+
return df, controls
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
def prepare_groups(df):
|
| 29 |
+
ever = df.groupby("id")["treatment"].max().rename("treated_group")
|
| 30 |
+
df = df.merge(ever, on="id", how="left")
|
| 31 |
+
df["treated_group"] = (df["treated_group"] > 0).astype(int)
|
| 32 |
+
return df
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
def find_candidate_pairs(df, max_questions=5):
|
| 36 |
+
times_sorted = sorted(df["time"].unique().tolist())
|
| 37 |
+
treated_by_time = df.groupby("time")["treatment"].sum()
|
| 38 |
+
post_times = [t for t in times_sorted if treated_by_time.get(t, 0) > 0]
|
| 39 |
+
pairs = []
|
| 40 |
+
if len(post_times) > 0:
|
| 41 |
+
t_post = min(post_times)
|
| 42 |
+
pre_candidates = [t for t in times_sorted if t < t_post]
|
| 43 |
+
if len(pre_candidates) > 0:
|
| 44 |
+
t_pre = max(pre_candidates)
|
| 45 |
+
pairs.append((t_pre, t_post))
|
| 46 |
+
t_first = times_sorted[0]
|
| 47 |
+
if t_first != t_pre:
|
| 48 |
+
pairs.append((t_first, t_post))
|
| 49 |
+
next_posts = [t for t in times_sorted if t > t_post]
|
| 50 |
+
if len(next_posts) > 0:
|
| 51 |
+
pairs.append((t_pre, next_posts[0]))
|
| 52 |
+
t_last = times_sorted[-1]
|
| 53 |
+
if t_last not in [t_post, t_pre]:
|
| 54 |
+
pairs.append((t_pre, t_last))
|
| 55 |
+
earlier_pre = [t for t in pre_candidates if t < t_pre]
|
| 56 |
+
if len(earlier_pre) > 0:
|
| 57 |
+
pairs.append((earlier_pre[-1], t_pre))
|
| 58 |
+
else:
|
| 59 |
+
for i in range(len(times_sorted) - 1):
|
| 60 |
+
pairs.append((times_sorted[i], times_sorted[i + 1]))
|
| 61 |
+
uniq = []
|
| 62 |
+
for p in pairs:
|
| 63 |
+
if p not in uniq and p[0] != p[1]:
|
| 64 |
+
uniq.append(p)
|
| 65 |
+
return uniq[:max_questions]
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
def fit_2x2_did(df, pre_t, post_t, controls):
|
| 69 |
+
dsub = df[df["time"].isin([pre_t, post_t])].copy()
|
| 70 |
+
counts = dsub.groupby("id")["time"].nunique()
|
| 71 |
+
keep = counts[counts == 2].index
|
| 72 |
+
dsub = dsub[dsub["id"].isin(keep)].copy()
|
| 73 |
+
if dsub["treated_group"].nunique() < 2:
|
| 74 |
+
return None
|
| 75 |
+
dsub["post_pair"] = (dsub["time"] == post_t).astype(int)
|
| 76 |
+
dsub["D_pair"] = dsub["treated_group"] * dsub["post_pair"]
|
| 77 |
+
base = ["D_pair", "treated_group", "post_pair"]
|
| 78 |
+
control_terms = []
|
| 79 |
+
inter = []
|
| 80 |
+
if "age" in controls and dsub["age"].notna().sum() > 0 and dsub["age"].nunique() > 1:
|
| 81 |
+
control_terms.append("age"); inter.append("post_pair:age")
|
| 82 |
+
if "sex" in controls and dsub["sex"].notna().sum() > 0 and dsub["sex"].nunique() > 1:
|
| 83 |
+
control_terms.append("sex"); inter.append("post_pair:sex")
|
| 84 |
+
if "educationClean" in controls and dsub["educationClean"].notna().sum() > 0 and dsub["educationClean"].nunique() > 1:
|
| 85 |
+
control_terms.append("educationClean"); inter.append("post_pair:educationClean")
|
| 86 |
+
if "income" in controls and dsub["income"].notna().sum() > 0 and dsub["income"].nunique() > 1:
|
| 87 |
+
control_terms.append("C(income)"); inter.append("post_pair:C(income)")
|
| 88 |
+
rhs = base + control_terms + inter
|
| 89 |
+
formula = "bi ~ " + " + ".join(rhs)
|
| 90 |
+
try:
|
| 91 |
+
res = smf.ols(formula=formula, data=dsub).fit(cov_type="cluster", cov_kwds={"groups": dsub["id"]})
|
| 92 |
+
except Exception:
|
| 93 |
+
return None
|
| 94 |
+
if "D_pair" not in res.params.index:
|
| 95 |
+
return None
|
| 96 |
+
coef = res.params.get("D_pair", np.nan)
|
| 97 |
+
se = res.bse.get("D_pair", np.nan)
|
| 98 |
+
return {"coef": float(coef), "se": float(se)}
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
if __name__ == "__main__":
|
| 102 |
+
if len(sys.argv) < 2:
|
| 103 |
+
raise SystemExit("Usage: python estimation_3.py data.csv")
|
| 104 |
+
path = sys.argv[1]
|
| 105 |
+
df, controls = load_data(path)
|
| 106 |
+
df = prepare_groups(df)
|
| 107 |
+
pairs = find_candidate_pairs(df, max_questions=5)
|
| 108 |
+
if len(pairs) <= TARGET_INDEX:
|
| 109 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 110 |
+
raise SystemExit(0)
|
| 111 |
+
pre_t, post_t = pairs[TARGET_INDEX]
|
| 112 |
+
res = fit_2x2_did(df, pre_t, post_t, controls)
|
| 113 |
+
if res is None:
|
| 114 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 115 |
+
else:
|
| 116 |
+
print(f"effect: {res['coef']} and std_error: {res['se']}")
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/estimation_4.py
ADDED
|
@@ -0,0 +1,116 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import sys
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
import numpy as np
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import statsmodels.formula.api as smf
|
| 6 |
+
|
| 7 |
+
# This script runs the 4th 2x2 DiD estimation (fourth candidate pair)
|
| 8 |
+
TARGET_INDEX = 3 # zero-based
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
def load_data(path):
|
| 12 |
+
df = pd.read_csv(path)
|
| 13 |
+
required = ["id", "time", "bi", "treatment"]
|
| 14 |
+
for c in required:
|
| 15 |
+
if c not in df.columns:
|
| 16 |
+
raise ValueError(f"Required column '{c}' not found")
|
| 17 |
+
df["id"] = pd.to_numeric(df["id"], errors="coerce")
|
| 18 |
+
df["time"] = pd.to_numeric(df["time"], errors="coerce")
|
| 19 |
+
df["bi"] = pd.to_numeric(df["bi"], errors="coerce")
|
| 20 |
+
df["treatment"] = pd.to_numeric(df["treatment"], errors="coerce")
|
| 21 |
+
df = df.dropna(subset=["id", "time", "bi", "treatment"]).copy()
|
| 22 |
+
df["id"] = df["id"].astype(int)
|
| 23 |
+
df["time"] = df["time"].astype(int)
|
| 24 |
+
controls = [c for c in ["age", "sex", "educationClean", "income"] if c in df.columns]
|
| 25 |
+
return df, controls
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
def prepare_groups(df):
|
| 29 |
+
ever = df.groupby("id")["treatment"].max().rename("treated_group")
|
| 30 |
+
df = df.merge(ever, on="id", how="left")
|
| 31 |
+
df["treated_group"] = (df["treated_group"] > 0).astype(int)
|
| 32 |
+
return df
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
def find_candidate_pairs(df, max_questions=5):
|
| 36 |
+
times_sorted = sorted(df["time"].unique().tolist())
|
| 37 |
+
treated_by_time = df.groupby("time")["treatment"].sum()
|
| 38 |
+
post_times = [t for t in times_sorted if treated_by_time.get(t, 0) > 0]
|
| 39 |
+
pairs = []
|
| 40 |
+
if len(post_times) > 0:
|
| 41 |
+
t_post = min(post_times)
|
| 42 |
+
pre_candidates = [t for t in times_sorted if t < t_post]
|
| 43 |
+
if len(pre_candidates) > 0:
|
| 44 |
+
t_pre = max(pre_candidates)
|
| 45 |
+
pairs.append((t_pre, t_post))
|
| 46 |
+
t_first = times_sorted[0]
|
| 47 |
+
if t_first != t_pre:
|
| 48 |
+
pairs.append((t_first, t_post))
|
| 49 |
+
next_posts = [t for t in times_sorted if t > t_post]
|
| 50 |
+
if len(next_posts) > 0:
|
| 51 |
+
pairs.append((t_pre, next_posts[0]))
|
| 52 |
+
t_last = times_sorted[-1]
|
| 53 |
+
if t_last not in [t_post, t_pre]:
|
| 54 |
+
pairs.append((t_pre, t_last))
|
| 55 |
+
earlier_pre = [t for t in pre_candidates if t < t_pre]
|
| 56 |
+
if len(earlier_pre) > 0:
|
| 57 |
+
pairs.append((earlier_pre[-1], t_pre))
|
| 58 |
+
else:
|
| 59 |
+
for i in range(len(times_sorted) - 1):
|
| 60 |
+
pairs.append((times_sorted[i], times_sorted[i + 1]))
|
| 61 |
+
uniq = []
|
| 62 |
+
for p in pairs:
|
| 63 |
+
if p not in uniq and p[0] != p[1]:
|
| 64 |
+
uniq.append(p)
|
| 65 |
+
return uniq[:max_questions]
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
def fit_2x2_did(df, pre_t, post_t, controls):
|
| 69 |
+
dsub = df[df["time"].isin([pre_t, post_t])].copy()
|
| 70 |
+
counts = dsub.groupby("id")["time"].nunique()
|
| 71 |
+
keep = counts[counts == 2].index
|
| 72 |
+
dsub = dsub[dsub["id"].isin(keep)].copy()
|
| 73 |
+
if dsub["treated_group"].nunique() < 2:
|
| 74 |
+
return None
|
| 75 |
+
dsub["post_pair"] = (dsub["time"] == post_t).astype(int)
|
| 76 |
+
dsub["D_pair"] = dsub["treated_group"] * dsub["post_pair"]
|
| 77 |
+
base = ["D_pair", "treated_group", "post_pair"]
|
| 78 |
+
control_terms = []
|
| 79 |
+
inter = []
|
| 80 |
+
if "age" in controls and dsub["age"].notna().sum() > 0 and dsub["age"].nunique() > 1:
|
| 81 |
+
control_terms.append("age"); inter.append("post_pair:age")
|
| 82 |
+
if "sex" in controls and dsub["sex"].notna().sum() > 0 and dsub["sex"].nunique() > 1:
|
| 83 |
+
control_terms.append("sex"); inter.append("post_pair:sex")
|
| 84 |
+
if "educationClean" in controls and dsub["educationClean"].notna().sum() > 0 and dsub["educationClean"].nunique() > 1:
|
| 85 |
+
control_terms.append("educationClean"); inter.append("post_pair:educationClean")
|
| 86 |
+
if "income" in controls and dsub["income"].notna().sum() > 0 and dsub["income"].nunique() > 1:
|
| 87 |
+
control_terms.append("C(income)"); inter.append("post_pair:C(income)")
|
| 88 |
+
rhs = base + control_terms + inter
|
| 89 |
+
formula = "bi ~ " + " + ".join(rhs)
|
| 90 |
+
try:
|
| 91 |
+
res = smf.ols(formula=formula, data=dsub).fit(cov_type="cluster", cov_kwds={"groups": dsub["id"]})
|
| 92 |
+
except Exception:
|
| 93 |
+
return None
|
| 94 |
+
if "D_pair" not in res.params.index:
|
| 95 |
+
return None
|
| 96 |
+
coef = res.params.get("D_pair", np.nan)
|
| 97 |
+
se = res.bse.get("D_pair", np.nan)
|
| 98 |
+
return {"coef": float(coef), "se": float(se)}
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
if __name__ == "__main__":
|
| 102 |
+
if len(sys.argv) < 2:
|
| 103 |
+
raise SystemExit("Usage: python estimation_4.py data.csv")
|
| 104 |
+
path = sys.argv[1]
|
| 105 |
+
df, controls = load_data(path)
|
| 106 |
+
df = prepare_groups(df)
|
| 107 |
+
pairs = find_candidate_pairs(df, max_questions=5)
|
| 108 |
+
if len(pairs) <= TARGET_INDEX:
|
| 109 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 110 |
+
raise SystemExit(0)
|
| 111 |
+
pre_t, post_t = pairs[TARGET_INDEX]
|
| 112 |
+
res = fit_2x2_did(df, pre_t, post_t, controls)
|
| 113 |
+
if res is None:
|
| 114 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 115 |
+
else:
|
| 116 |
+
print(f"effect: {res['coef']} and std_error: {res['se']}")
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/estimation_5.py
ADDED
|
@@ -0,0 +1,116 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import sys
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
import numpy as np
|
| 4 |
+
import pandas as pd
|
| 5 |
+
import statsmodels.formula.api as smf
|
| 6 |
+
|
| 7 |
+
# This script runs the 5th 2x2 DiD estimation (fifth candidate pair)
|
| 8 |
+
TARGET_INDEX = 4 # zero-based
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
def load_data(path):
|
| 12 |
+
df = pd.read_csv(path)
|
| 13 |
+
required = ["id", "time", "bi", "treatment"]
|
| 14 |
+
for c in required:
|
| 15 |
+
if c not in df.columns:
|
| 16 |
+
raise ValueError(f"Required column '{c}' not found")
|
| 17 |
+
df["id"] = pd.to_numeric(df["id"], errors="coerce")
|
| 18 |
+
df["time"] = pd.to_numeric(df["time"], errors="coerce")
|
| 19 |
+
df["bi"] = pd.to_numeric(df["bi"], errors="coerce")
|
| 20 |
+
df["treatment"] = pd.to_numeric(df["treatment"], errors="coerce")
|
| 21 |
+
df = df.dropna(subset=["id", "time", "bi", "treatment"]).copy()
|
| 22 |
+
df["id"] = df["id"].astype(int)
|
| 23 |
+
df["time"] = df["time"].astype(int)
|
| 24 |
+
controls = [c for c in ["age", "sex", "educationClean", "income"] if c in df.columns]
|
| 25 |
+
return df, controls
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
def prepare_groups(df):
|
| 29 |
+
ever = df.groupby("id")["treatment"].max().rename("treated_group")
|
| 30 |
+
df = df.merge(ever, on="id", how="left")
|
| 31 |
+
df["treated_group"] = (df["treated_group"] > 0).astype(int)
|
| 32 |
+
return df
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
def find_candidate_pairs(df, max_questions=5):
|
| 36 |
+
times_sorted = sorted(df["time"].unique().tolist())
|
| 37 |
+
treated_by_time = df.groupby("time")["treatment"].sum()
|
| 38 |
+
post_times = [t for t in times_sorted if treated_by_time.get(t, 0) > 0]
|
| 39 |
+
pairs = []
|
| 40 |
+
if len(post_times) > 0:
|
| 41 |
+
t_post = min(post_times)
|
| 42 |
+
pre_candidates = [t for t in times_sorted if t < t_post]
|
| 43 |
+
if len(pre_candidates) > 0:
|
| 44 |
+
t_pre = max(pre_candidates)
|
| 45 |
+
pairs.append((t_pre, t_post))
|
| 46 |
+
t_first = times_sorted[0]
|
| 47 |
+
if t_first != t_pre:
|
| 48 |
+
pairs.append((t_first, t_post))
|
| 49 |
+
next_posts = [t for t in times_sorted if t > t_post]
|
| 50 |
+
if len(next_posts) > 0:
|
| 51 |
+
pairs.append((t_pre, next_posts[0]))
|
| 52 |
+
t_last = times_sorted[-1]
|
| 53 |
+
if t_last not in [t_post, t_pre]:
|
| 54 |
+
pairs.append((t_pre, t_last))
|
| 55 |
+
earlier_pre = [t for t in pre_candidates if t < t_pre]
|
| 56 |
+
if len(earlier_pre) > 0:
|
| 57 |
+
pairs.append((earlier_pre[-1], t_pre))
|
| 58 |
+
else:
|
| 59 |
+
for i in range(len(times_sorted) - 1):
|
| 60 |
+
pairs.append((times_sorted[i], times_sorted[i + 1]))
|
| 61 |
+
uniq = []
|
| 62 |
+
for p in pairs:
|
| 63 |
+
if p not in uniq and p[0] != p[1]:
|
| 64 |
+
uniq.append(p)
|
| 65 |
+
return uniq[:max_questions]
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
def fit_2x2_did(df, pre_t, post_t, controls):
|
| 69 |
+
dsub = df[df["time"].isin([pre_t, post_t])].copy()
|
| 70 |
+
counts = dsub.groupby("id")["time"].nunique()
|
| 71 |
+
keep = counts[counts == 2].index
|
| 72 |
+
dsub = dsub[dsub["id"].isin(keep)].copy()
|
| 73 |
+
if dsub["treated_group"].nunique() < 2:
|
| 74 |
+
return None
|
| 75 |
+
dsub["post_pair"] = (dsub["time"] == post_t).astype(int)
|
| 76 |
+
dsub["D_pair"] = dsub["treated_group"] * dsub["post_pair"]
|
| 77 |
+
base = ["D_pair", "treated_group", "post_pair"]
|
| 78 |
+
control_terms = []
|
| 79 |
+
inter = []
|
| 80 |
+
if "age" in controls and dsub["age"].notna().sum() > 0 and dsub["age"].nunique() > 1:
|
| 81 |
+
control_terms.append("age"); inter.append("post_pair:age")
|
| 82 |
+
if "sex" in controls and dsub["sex"].notna().sum() > 0 and dsub["sex"].nunique() > 1:
|
| 83 |
+
control_terms.append("sex"); inter.append("post_pair:sex")
|
| 84 |
+
if "educationClean" in controls and dsub["educationClean"].notna().sum() > 0 and dsub["educationClean"].nunique() > 1:
|
| 85 |
+
control_terms.append("educationClean"); inter.append("post_pair:educationClean")
|
| 86 |
+
if "income" in controls and dsub["income"].notna().sum() > 0 and dsub["income"].nunique() > 1:
|
| 87 |
+
control_terms.append("C(income)"); inter.append("post_pair:C(income)")
|
| 88 |
+
rhs = base + control_terms + inter
|
| 89 |
+
formula = "bi ~ " + " + ".join(rhs)
|
| 90 |
+
try:
|
| 91 |
+
res = smf.ols(formula=formula, data=dsub).fit(cov_type="cluster", cov_kwds={"groups": dsub["id"]})
|
| 92 |
+
except Exception:
|
| 93 |
+
return None
|
| 94 |
+
if "D_pair" not in res.params.index:
|
| 95 |
+
return None
|
| 96 |
+
coef = res.params.get("D_pair", np.nan)
|
| 97 |
+
se = res.bse.get("D_pair", np.nan)
|
| 98 |
+
return {"coef": float(coef), "se": float(se)}
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
if __name__ == "__main__":
|
| 102 |
+
if len(sys.argv) < 2:
|
| 103 |
+
raise SystemExit("Usage: python estimation_5.py data.csv")
|
| 104 |
+
path = sys.argv[1]
|
| 105 |
+
df, controls = load_data(path)
|
| 106 |
+
df = prepare_groups(df)
|
| 107 |
+
pairs = find_candidate_pairs(df, max_questions=5)
|
| 108 |
+
if len(pairs) <= TARGET_INDEX:
|
| 109 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 110 |
+
raise SystemExit(0)
|
| 111 |
+
pre_t, post_t = pairs[TARGET_INDEX]
|
| 112 |
+
res = fit_2x2_did(df, pre_t, post_t, controls)
|
| 113 |
+
if res is None:
|
| 114 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 115 |
+
else:
|
| 116 |
+
print(f"effect: {res['coef']} and std_error: {res['se']}")
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/output_1.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
effect: -0.05039967041170819 and std_error: 0.018595278423414588
|
| 2 |
+
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/output_2.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
effect: nan and std_error: nan
|
| 2 |
+
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/output_3.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
effect: nan and std_error: nan
|
| 2 |
+
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/output_4.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
effect: nan and std_error: nan
|
| 2 |
+
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/estimation/output_5.txt
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
effect: nan and std_error: nan
|
| 2 |
+
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/finding_1.json
ADDED
|
@@ -0,0 +1,38 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"identification_strategy": {
|
| 3 |
+
"strategy": "Difference-in-Differences",
|
| 4 |
+
"variant": "sharp 2x2 (pre=2, post=3; ever-treated vs never-treated)",
|
| 5 |
+
"treatments": [
|
| 6 |
+
"treatment"
|
| 7 |
+
],
|
| 8 |
+
"outcomes": [
|
| 9 |
+
"bi"
|
| 10 |
+
],
|
| 11 |
+
"outcome_is_stacked": false,
|
| 12 |
+
"controls": [
|
| 13 |
+
"age",
|
| 14 |
+
"sex",
|
| 15 |
+
"educationClean",
|
| 16 |
+
"income"
|
| 17 |
+
],
|
| 18 |
+
"post_treatment_variables": null,
|
| 19 |
+
"minimal_controlling_set": null,
|
| 20 |
+
"reason_for_minimal_controlling_set": null,
|
| 21 |
+
"time_variable": "time",
|
| 22 |
+
"group_variable": "id"
|
| 23 |
+
},
|
| 24 |
+
"quantity": "ATT",
|
| 25 |
+
"estimand_population": "treated (ever-treated units observed in both periods)",
|
| 26 |
+
"quantity_value": -0.05039967041170819,
|
| 27 |
+
"quantity_ci": {
|
| 28 |
+
"lower": -0.08684641612160078,
|
| 29 |
+
"upper": -0.013952924701815597,
|
| 30 |
+
"level": 0.95
|
| 31 |
+
},
|
| 32 |
+
"standard_error": 0.018595278423414588,
|
| 33 |
+
"p_value": 0.006721270480226373,
|
| 34 |
+
"effect_units": "units of bi (0-1 scale)",
|
| 35 |
+
"subgroup": null,
|
| 36 |
+
"exact_causal_question": "ATT of treatment on bi using a 2x2 DiD with pre=2 and post=3, comparing ever-treated units (id with treatment=1 in any period) to never-treated units.",
|
| 37 |
+
"layman_query": "How did the change between time 2 and 3 affect the outcome for treated individuals compared to untreated individuals?"
|
| 38 |
+
}
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/finding_1_double_ml.json
ADDED
|
@@ -0,0 +1,38 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"identification_strategy": {
|
| 3 |
+
"strategy": "Difference-in-Differences",
|
| 4 |
+
"variant": "sharp 2x2 (pre=2, post=3; ever-treated vs never-treated)",
|
| 5 |
+
"treatments": [
|
| 6 |
+
"treatment"
|
| 7 |
+
],
|
| 8 |
+
"outcomes": [
|
| 9 |
+
"bi"
|
| 10 |
+
],
|
| 11 |
+
"outcome_is_stacked": false,
|
| 12 |
+
"controls": [
|
| 13 |
+
"age",
|
| 14 |
+
"sex",
|
| 15 |
+
"educationClean",
|
| 16 |
+
"income"
|
| 17 |
+
],
|
| 18 |
+
"post_treatment_variables": null,
|
| 19 |
+
"minimal_controlling_set": null,
|
| 20 |
+
"reason_for_minimal_controlling_set": null,
|
| 21 |
+
"time_variable": "time",
|
| 22 |
+
"group_variable": "id"
|
| 23 |
+
},
|
| 24 |
+
"quantity": "ATT",
|
| 25 |
+
"estimand_population": "treated (ever-treated units observed in both periods)",
|
| 26 |
+
"quantity_value": -0.09027275024673113,
|
| 27 |
+
"quantity_ci": {
|
| 28 |
+
"lower": -0.15800576471404815,
|
| 29 |
+
"upper": -0.022539735779414122,
|
| 30 |
+
"level": 0.95
|
| 31 |
+
},
|
| 32 |
+
"standard_error": 0.03455766044250868,
|
| 33 |
+
"p_value": 0.008995224547031567,
|
| 34 |
+
"effect_units": "units of bi (0-1 scale)",
|
| 35 |
+
"subgroup": null,
|
| 36 |
+
"exact_causal_question": "ATT of treatment on bi using a 2x2 DiD with pre=2 and post=3, comparing ever-treated units (id with treatment=1 in any period) to never-treated units.",
|
| 37 |
+
"layman_query": "How did the change between time 2 and 3 affect the outcome for treated individuals compared to untreated individuals?"
|
| 38 |
+
}
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/out_1.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
File [35m"/Users/ayushsawarni/Projects/CAUSAL_BENCHMARK/DiD-replication/replication/processed/Bisgaard_Slothuus_2018/generate_causal_questions.py"[0m, line [35m1[0m
|
| 3 |
+
[1;31m`[0m``python
|
| 4 |
+
[1;31m^[0m
|
| 5 |
+
[1;35mSyntaxError[0m: [35minvalid syntax[0m
|
| 6 |
+
|
| 7 |
+
ERROR conda.cli.main_run:execute(47): `conda run python generate_causal_questions.py ../../rawdata_csv/Bisgaard_Slothuus_2018_AJPS.csv` failed. (See above for error)
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/out_2.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/out_dml_1.txt
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
|
repo-type=dataset/research_papers/DiD/Bisgaard_Slothuus_2018/paper/Bisgaard_Slothuus_2018.pdf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7b4163994a792bba78c17f87963d0285cf0280827fc92d908ca162a6787afd5a
|
| 3 |
+
size 371263
|
repo-type=dataset/research_papers/DiD/Blair_etal_2022/data/Blair_etal_2022_JOP.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
repo-type=dataset/research_papers/DiD/Blair_etal_2022/data/metadata.txt
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
| Variable Name | Description |
|
| 2 |
+
|---------------|-------------------------------------------------------------------------------------------------------------|
|
| 3 |
+
| dv | Natural log of total annual non‑fuel mineral exploration investment (constant 1997 USD) plus one, by country-year. |
|
| 4 |
+
| ucdp_lead | Indicator equal to 1 if UCDP-GED records at least one fatal armed conflict in the country in year t or t−1; 0 otherwise. |
|
| 5 |
+
| country | Country name corresponding to the observation. |
|
| 6 |
+
| year | Calendar year of the observation (1997–2014). |
|
| 7 |
+
|
| 8 |
+
Data Description: This dataset is a global country–year panel (1997–2014) built to study how armed conflict relates to mining-sector investment. Investment data come from SNL Metals & Mining’s survey-based compilation of exploration budgets and company reports, covering non‑fuel minerals (e.g., base metals, gold, diamonds) and deflated to constant 1997 USD; these firm-level figures are aggregated to the country–year level. Conflict information is derived from the Uppsala Conflict Data Program’s Georeferenced Event Dataset (UCDP GED) and is used to indicate whether a country experienced at least one fatal armed conflict in the current or previous year. The resulting dataset links countries, years, exploration spending, and conflict status to provide comprehensive coverage across 177 countries during the period of analysis.
|
repo-type=dataset/research_papers/DiD/Blair_etal_2022/estimation/all_q.py
ADDED
|
@@ -0,0 +1,238 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
| 1 |
+
import sys
|
| 2 |
+
import json
|
| 3 |
+
import argparse
|
| 4 |
+
from pathlib import Path
|
| 5 |
+
import numpy as np
|
| 6 |
+
import pandas as pd
|
| 7 |
+
import statsmodels.api as sm
|
| 8 |
+
|
| 9 |
+
def build_2x2(df, pre_years, post_years, y_col="dv", d_col="ucdp_lead", unit_col="country", time_col="year"):
|
| 10 |
+
# Keep only pre/post windows
|
| 11 |
+
df2 = df[df[time_col].isin(pre_years + post_years)].copy()
|
| 12 |
+
if df2.empty:
|
| 13 |
+
return None
|
| 14 |
+
|
| 15 |
+
# Require at least one obs in both windows per unit
|
| 16 |
+
have_pre = df2[df2[time_col].isin(pre_years)].groupby(unit_col)[y_col].count()
|
| 17 |
+
have_post = df2[df2[time_col].isin(post_years)].groupby(unit_col)[y_col].count()
|
| 18 |
+
eligible_units = set(have_pre[have_pre > 0].index).intersection(set(have_post[have_post > 0].index))
|
| 19 |
+
df2 = df2[df2[unit_col].isin(eligible_units)].copy()
|
| 20 |
+
if df2.empty:
|
| 21 |
+
return None
|
| 22 |
+
|
| 23 |
+
# Compute average outcome in pre and post per unit
|
| 24 |
+
pre = (
|
| 25 |
+
df2[df2[time_col].isin(pre_years)]
|
| 26 |
+
.groupby(unit_col)
|
| 27 |
+
.agg({y_col: "mean", d_col: "mean"})
|
| 28 |
+
.rename(columns={y_col: "y_pre", d_col: "d_pre"})
|
| 29 |
+
)
|
| 30 |
+
post = (
|
| 31 |
+
df2[df2[time_col].isin(post_years)]
|
| 32 |
+
.groupby(unit_col)
|
| 33 |
+
.agg({y_col: "mean", d_col: "mean"})
|
| 34 |
+
.rename(columns={y_col: "y_post", d_col: "d_post"})
|
| 35 |
+
)
|
| 36 |
+
agg = pre.join(post, how="inner")
|
| 37 |
+
if agg.empty:
|
| 38 |
+
return None
|
| 39 |
+
|
| 40 |
+
# Treated: no conflict in pre and conflict appears in post
|
| 41 |
+
treated_units = agg[(agg["d_pre"] == 0) & (agg["d_post"] > 0)].index.tolist()
|
| 42 |
+
# Control: no conflict in both pre and post
|
| 43 |
+
control_units = agg[(agg["d_pre"] == 0) & (agg["d_post"] == 0)].index.tolist()
|
| 44 |
+
|
| 45 |
+
if len(treated_units) == 0 or len(control_units) == 0:
|
| 46 |
+
return None
|
| 47 |
+
|
| 48 |
+
# Build long 2-period panel (average per period)
|
| 49 |
+
treated_df = pd.DataFrame({
|
| 50 |
+
unit_col: np.repeat(treated_units, 2),
|
| 51 |
+
"post": [0, 1] * len(treated_units),
|
| 52 |
+
"treated": 1
|
| 53 |
+
})
|
| 54 |
+
control_df = pd.DataFrame({
|
| 55 |
+
unit_col: np.repeat(control_units, 2),
|
| 56 |
+
"post": [0, 1] * len(control_units),
|
| 57 |
+
"treated": 0
|
| 58 |
+
})
|
| 59 |
+
panel = pd.concat([treated_df, control_df], ignore_index=True)
|
| 60 |
+
|
| 61 |
+
# Map outcomes to pre/post
|
| 62 |
+
y_map_pre = agg["y_pre"].to_dict()
|
| 63 |
+
y_map_post = agg["y_post"].to_dict()
|
| 64 |
+
|
| 65 |
+
def map_y(row):
|
| 66 |
+
return y_map_post[row[unit_col]] if row["post"] == 1 else y_map_pre[row[unit_col]]
|
| 67 |
+
|
| 68 |
+
panel[y_col] = panel.apply(map_y, axis=1)
|
| 69 |
+
|
| 70 |
+
# Cluster by unit for SEs
|
| 71 |
+
panel["cluster"] = panel[unit_col].astype("category").cat.codes
|
| 72 |
+
return panel
|
| 73 |
+
|
| 74 |
+
def did_ols(panel, y_col="dv"):
|
| 75 |
+
X = panel[["treated", "post"]].copy()
|
| 76 |
+
X["interaction"] = panel["treated"] * panel["post"]
|
| 77 |
+
X = sm.add_constant(X)
|
| 78 |
+
y = panel[y_col].astype(float)
|
| 79 |
+
model = sm.OLS(y, X)
|
| 80 |
+
res = model.fit(cov_type="cluster", cov_kwds={"groups": panel["cluster"]})
|
| 81 |
+
att = res.params["interaction"]
|
| 82 |
+
se = res.bse["interaction"]
|
| 83 |
+
ci_low = att - 1.96 * se
|
| 84 |
+
ci_high = att + 1.96 * se
|
| 85 |
+
pval = res.pvalues["interaction"]
|
| 86 |
+
return att, se, (ci_low, ci_high), pval
|
| 87 |
+
|
| 88 |
+
def main():
|
| 89 |
+
parser = argparse.ArgumentParser()
|
| 90 |
+
parser.add_argument("csv_path", type=str, help="Path to CSV data file")
|
| 91 |
+
args = parser.parse_args()
|
| 92 |
+
csv_path = Path(args.csv_path)
|
| 93 |
+
|
| 94 |
+
# Load data
|
| 95 |
+
df = pd.read_csv(csv_path)
|
| 96 |
+
# Drop unnamed index columns if present
|
| 97 |
+
df = df.loc[:, ~df.columns.astype(str).str.contains("^Unnamed")]
|
| 98 |
+
# Basic cleaning and types
|
| 99 |
+
if "dv" not in df.columns or "ucdp_lead" not in df.columns or "country" not in df.columns or "year" not in df.columns:
|
| 100 |
+
raise ValueError("Input CSV must contain columns: dv, ucdp_lead, country, year")
|
| 101 |
+
|
| 102 |
+
df["dv"] = pd.to_numeric(df["dv"], errors="coerce")
|
| 103 |
+
df["ucdp_lead"] = pd.to_numeric(df["ucdp_lead"], errors="coerce")
|
| 104 |
+
df["year"] = pd.to_numeric(df["year"], errors="coerce")
|
| 105 |
+
df["country"] = df["country"].astype(str)
|
| 106 |
+
|
| 107 |
+
df = df.dropna(subset=["dv", "ucdp_lead", "country", "year"]).copy()
|
| 108 |
+
df["year"] = df["year"].astype(int)
|
| 109 |
+
df["ucdp_lead"] = (df["ucdp_lead"] > 0).astype(int)
|
| 110 |
+
|
| 111 |
+
min_y, max_y = df["year"].min(), df["year"].max()
|
| 112 |
+
|
| 113 |
+
# Define candidate 3-year pre/post windows within available years
|
| 114 |
+
candidate_windows = [
|
| 115 |
+
(list(range(1997, 2000)), list(range(2000, 2003))),
|
| 116 |
+
(list(range(2000, 2003)), list(range(2003, 2006))),
|
| 117 |
+
(list(range(2003, 2006)), list(range(2006, 2009))),
|
| 118 |
+
(list(range(2006, 2009)), list(range(2009, 2012))),
|
| 119 |
+
(list(range(2009, 2012)), list(range(2012, 2015))),
|
| 120 |
+
]
|
| 121 |
+
|
| 122 |
+
windows = []
|
| 123 |
+
for pre, post in candidate_windows:
|
| 124 |
+
if (min(pre) >= min_y) and (max(post) <= max_y):
|
| 125 |
+
windows.append((pre, post))
|
| 126 |
+
|
| 127 |
+
questions_built = 0
|
| 128 |
+
q_idx = 1
|
| 129 |
+
for pre, post in windows:
|
| 130 |
+
panel = build_2x2(df, pre, post, y_col="dv", d_col="ucdp_lead", unit_col="country", time_col="year")
|
| 131 |
+
if panel is None:
|
| 132 |
+
continue
|
| 133 |
+
try:
|
| 134 |
+
att, se, (lb, ub), pval = did_ols(panel, y_col="dv")
|
| 135 |
+
except Exception:
|
| 136 |
+
continue
|
| 137 |
+
|
| 138 |
+
pre_str = f"{min(pre)}–{max(pre)}"
|
| 139 |
+
post_str = f"{min(post)}–{max(post)}"
|
| 140 |
+
exact_q = (
|
| 141 |
+
f"Among countries with no armed conflict in {pre_str}, what is the ATT of experiencing at least one "
|
| 142 |
+
f"fatal armed conflict in {post_str} (vs none) on log exploration investment, using a 2×2 DiD with "
|
| 143 |
+
f"treated={{no conflict in pre, conflict in post}} and controls={{no conflict in both periods}}?"
|
| 144 |
+
)
|
| 145 |
+
layman = (
|
| 146 |
+
f"Did investment change for countries that newly faced conflict in {post_str} compared to similar "
|
| 147 |
+
f"countries that stayed peaceful in both {pre_str} and {post_str}?"
|
| 148 |
+
)
|
| 149 |
+
|
| 150 |
+
out = {
|
| 151 |
+
"identification_strategy": {
|
| 152 |
+
"strategy": "Difference-in-Differences",
|
| 153 |
+
"variant": f"sharp 2x2 (pre: {pre_str}; post: {post_str})",
|
| 154 |
+
"treatments": ["ucdp_lead"],
|
| 155 |
+
"outcomes": ["dv"],
|
| 156 |
+
"outcome_is_stacked": False,
|
| 157 |
+
"controls": None,
|
| 158 |
+
"post_treatment_variables": None,
|
| 159 |
+
"minimal_controlling_set": None,
|
| 160 |
+
"reason_for_minimal_controlling_set": None,
|
| 161 |
+
"time_variable": "year",
|
| 162 |
+
"group_variable": "country"
|
| 163 |
+
},
|
| 164 |
+
"quantity": "ATT",
|
| 165 |
+
"estimand_population": "Countries with no conflict in pre period",
|
| 166 |
+
"quantity_value": float(att),
|
| 167 |
+
"quantity_ci": {
|
| 168 |
+
"lower": float(lb),
|
| 169 |
+
"upper": float(ub),
|
| 170 |
+
"level": 0.95
|
| 171 |
+
},
|
| 172 |
+
"standard_error": float(se),
|
| 173 |
+
"p_value": float(pval),
|
| 174 |
+
"effect_units": "log points",
|
| 175 |
+
"subgroup": None,
|
| 176 |
+
"exact_causal_question": exact_q,
|
| 177 |
+
"layman_query": layman
|
| 178 |
+
}
|
| 179 |
+
|
| 180 |
+
with open(f"question_{q_idx}.json", "w") as f:
|
| 181 |
+
json.dump(out, f, indent=2)
|
| 182 |
+
q_idx += 1
|
| 183 |
+
questions_built += 1
|
| 184 |
+
if questions_built >= 5:
|
| 185 |
+
break
|
| 186 |
+
|
| 187 |
+
# Fallback if no questions built
|
| 188 |
+
if questions_built == 0:
|
| 189 |
+
mid = int(np.median(df["year"]))
|
| 190 |
+
pre = list(range(max(min_y, mid - 2), mid + 1))
|
| 191 |
+
post = list(range(mid + 1, min(max_y, mid + 3) + 1))
|
| 192 |
+
panel = build_2x2(df, pre, post, y_col="dv", d_col="ucdp_lead", unit_col="country", time_col="year")
|
| 193 |
+
if panel is not None:
|
| 194 |
+
att, se, (lb, ub), pval = did_ols(panel, y_col="dv")
|
| 195 |
+
pre_str = f"{min(pre)}–{max(pre)}"
|
| 196 |
+
post_str = f"{min(post)}–{max(post)}"
|
| 197 |
+
exact_q = (
|
| 198 |
+
f"Among countries with no armed conflict in {pre_str}, what is the ATT of experiencing at least one "
|
| 199 |
+
f"fatal armed conflict in {post_str} (vs none) on log exploration investment, using a 2×2 DiD?"
|
| 200 |
+
)
|
| 201 |
+
layman = (
|
| 202 |
+
f"Did investment change for countries that newly faced conflict in {post_str} compared to similar "
|
| 203 |
+
f"countries that stayed peaceful in both {pre_str} and {post_str}?"
|
| 204 |
+
)
|
| 205 |
+
out = {
|
| 206 |
+
"identification_strategy": {
|
| 207 |
+
"strategy": "Difference-in-Differences",
|
| 208 |
+
"variant": f"sharp 2x2 (pre: {pre_str}; post: {post_str})",
|
| 209 |
+
"treatments": ["ucdp_lead"],
|
| 210 |
+
"outcomes": ["dv"],
|
| 211 |
+
"outcome_is_stacked": False,
|
| 212 |
+
"controls": None,
|
| 213 |
+
"post_treatment_variables": None,
|
| 214 |
+
"minimal_controlling_set": None,
|
| 215 |
+
"reason_for_minimal_controlling_set": None,
|
| 216 |
+
"time_variable": "year",
|
| 217 |
+
"group_variable": "country"
|
| 218 |
+
},
|
| 219 |
+
"quantity": "ATT",
|
| 220 |
+
"estimand_population": "Countries with no conflict in pre period",
|
| 221 |
+
"quantity_value": float(att),
|
| 222 |
+
"quantity_ci": {
|
| 223 |
+
"lower": float(lb),
|
| 224 |
+
"upper": float(ub),
|
| 225 |
+
"level": 0.95
|
| 226 |
+
},
|
| 227 |
+
"standard_error": float(se),
|
| 228 |
+
"p_value": float(pval),
|
| 229 |
+
"effect_units": "log points",
|
| 230 |
+
"subgroup": None,
|
| 231 |
+
"exact_causal_question": exact_q,
|
| 232 |
+
"layman_query": layman
|
| 233 |
+
}
|
| 234 |
+
with open("question_1.json", "w") as f:
|
| 235 |
+
json.dump(out, f, indent=2)
|
| 236 |
+
|
| 237 |
+
if __name__ == "__main__":
|
| 238 |
+
main()
|
repo-type=dataset/research_papers/DiD/Blair_etal_2022/estimation/estimation_1.py
ADDED
|
@@ -0,0 +1,107 @@
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
|
|
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|
|
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|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
import argparse
|
| 3 |
+
from pathlib import Path
|
| 4 |
+
import numpy as np
|
| 5 |
+
import pandas as pd
|
| 6 |
+
import statsmodels.api as sm
|
| 7 |
+
|
| 8 |
+
def build_2x2(df, pre_years, post_years, y_col="dv", d_col="ucdp_lead", unit_col="country", time_col="year"):
|
| 9 |
+
df2 = df[df[time_col].isin(pre_years + post_years)].copy()
|
| 10 |
+
if df2.empty:
|
| 11 |
+
return None
|
| 12 |
+
have_pre = df2[df2[time_col].isin(pre_years)].groupby(unit_col)[y_col].count()
|
| 13 |
+
have_post = df2[df2[time_col].isin(post_years)].groupby(unit_col)[y_col].count()
|
| 14 |
+
eligible_units = set(have_pre[have_pre > 0].index).intersection(set(have_post[have_post > 0].index))
|
| 15 |
+
df2 = df2[df2[unit_col].isin(eligible_units)].copy()
|
| 16 |
+
if df2.empty:
|
| 17 |
+
return None
|
| 18 |
+
pre = (
|
| 19 |
+
df2[df2[time_col].isin(pre_years)]
|
| 20 |
+
.groupby(unit_col)
|
| 21 |
+
.agg({y_col: "mean", d_col: "mean"})
|
| 22 |
+
.rename(columns={y_col: "y_pre", d_col: "d_pre"})
|
| 23 |
+
)
|
| 24 |
+
post = (
|
| 25 |
+
df2[df2[time_col].isin(post_years)]
|
| 26 |
+
.groupby(unit_col)
|
| 27 |
+
.agg({y_col: "mean", d_col: "mean"})
|
| 28 |
+
.rename(columns={y_col: "y_post", d_col: "d_post"})
|
| 29 |
+
)
|
| 30 |
+
agg = pre.join(post, how="inner")
|
| 31 |
+
if agg.empty:
|
| 32 |
+
return None
|
| 33 |
+
treated_units = agg[(agg["d_pre"] == 0) & (agg["d_post"] > 0)].index.tolist()
|
| 34 |
+
control_units = agg[(agg["d_pre"] == 0) & (agg["d_post"] == 0)].index.tolist()
|
| 35 |
+
if len(treated_units) == 0 or len(control_units) == 0:
|
| 36 |
+
return None
|
| 37 |
+
treated_df = pd.DataFrame({
|
| 38 |
+
unit_col: np.repeat(treated_units, 2),
|
| 39 |
+
"post": [0, 1] * len(treated_units),
|
| 40 |
+
"treated": 1
|
| 41 |
+
})
|
| 42 |
+
control_df = pd.DataFrame({
|
| 43 |
+
unit_col: np.repeat(control_units, 2),
|
| 44 |
+
"post": [0, 1] * len(control_units),
|
| 45 |
+
"treated": 0
|
| 46 |
+
})
|
| 47 |
+
panel = pd.concat([treated_df, control_df], ignore_index=True)
|
| 48 |
+
y_map_pre = agg["y_pre"].to_dict()
|
| 49 |
+
y_map_post = agg["y_post"].to_dict()
|
| 50 |
+
def map_y(row):
|
| 51 |
+
return y_map_post[row[unit_col]] if row["post"] == 1 else y_map_pre[row[unit_col]]
|
| 52 |
+
panel[y_col] = panel.apply(map_y, axis=1)
|
| 53 |
+
panel["cluster"] = panel[unit_col].astype("category").cat.codes
|
| 54 |
+
return panel
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
def did_ols(panel, y_col="dv"):
|
| 58 |
+
X = panel[["treated", "post"]].copy()
|
| 59 |
+
X["interaction"] = panel["treated"] * panel["post"]
|
| 60 |
+
X = sm.add_constant(X)
|
| 61 |
+
y = panel[y_col].astype(float)
|
| 62 |
+
model = sm.OLS(y, X)
|
| 63 |
+
res = model.fit(cov_type="cluster", cov_kwds={"groups": panel["cluster"]})
|
| 64 |
+
att = float(res.params["interaction"])
|
| 65 |
+
se = float(res.bse["interaction"])
|
| 66 |
+
return att, se
|
| 67 |
+
|
| 68 |
+
|
| 69 |
+
def main():
|
| 70 |
+
parser = argparse.ArgumentParser()
|
| 71 |
+
parser.add_argument("csv_path", type=str)
|
| 72 |
+
args = parser.parse_args()
|
| 73 |
+
p = Path(args.csv_path)
|
| 74 |
+
df = pd.read_csv(p)
|
| 75 |
+
df = df.loc[:, ~df.columns.astype(str).str.contains("^Unnamed")]
|
| 76 |
+
required = {"dv", "ucdp_lead", "country", "year"}
|
| 77 |
+
if not required.issubset(set(df.columns)):
|
| 78 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 79 |
+
return
|
| 80 |
+
df["dv"] = pd.to_numeric(df["dv"], errors="coerce")
|
| 81 |
+
df["ucdp_lead"] = pd.to_numeric(df["ucdp_lead"], errors="coerce")
|
| 82 |
+
df["year"] = pd.to_numeric(df["year"], errors="coerce")
|
| 83 |
+
df["country"] = df["country"].astype(str)
|
| 84 |
+
df = df.dropna(subset=["dv", "ucdp_lead", "country", "year"]).copy()
|
| 85 |
+
if df.empty:
|
| 86 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 87 |
+
return
|
| 88 |
+
df["year"] = df["year"].astype(int)
|
| 89 |
+
df["ucdp_lead"] = (df["ucdp_lead"] > 0).astype(int)
|
| 90 |
+
min_y, max_y = df["year"].min(), df["year"].max()
|
| 91 |
+
pre = list(range(1997, 2000))
|
| 92 |
+
post = list(range(2000, 2003))
|
| 93 |
+
if min(pre) < min_y or max(post) > max_y:
|
| 94 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 95 |
+
return
|
| 96 |
+
try:
|
| 97 |
+
panel = build_2x2(df, pre, post)
|
| 98 |
+
if panel is None:
|
| 99 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 100 |
+
return
|
| 101 |
+
att, se = did_ols(panel)
|
| 102 |
+
print(f"effect: {att} and std_error: {se}")
|
| 103 |
+
except Exception:
|
| 104 |
+
print(f"effect: {np.nan} and std_error: {np.nan}")
|
| 105 |
+
|
| 106 |
+
if __name__ == '__main__':
|
| 107 |
+
main()
|